explore NLP.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Yelp Dataset\n",
    "[**The Yelp Dataset**](https://www.yelp.com/dataset_challenge/) is a dataset published by the business review service [Yelp](http://yelp.com) for academic research and educational purposes. I really like the Yelp dataset as a subject for machine learning and natural language processing demos, because it's big (but not so big that you need your own data center to process it), well-connected, and anyone can relate to it &mdash; it's largely about food, after all!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\"business_id\": \"FYWN1wneV18bWNgQjJ2GNg\", \"name\": \"Dental by Design\", \"neighborhood\": \"\", \"address\": \"4855 E Warner Rd, Ste B9\", \"city\": \"Ahwatukee\", \"state\": \"AZ\", \"postal_code\": \"85044\", \"latitude\": 33.3306902, \"longitude\": -111.9785992, \"stars\": 4.0, \"review_count\": 22, \"is_open\": 1, \"attributes\": {\"AcceptsInsurance\": true, \"ByAppointmentOnly\": true, \"BusinessAcceptsCreditCards\": true}, \"categories\": [\"Dentists\", \"General Dentistry\", \"Health & Medical\", \"Oral Surgeons\", \"Cosmetic Dentists\", \"Orthodontists\"], \"hours\": {\"Friday\": \"7:30-17:00\", \"Tuesday\": \"7:30-17:00\", \"Thursday\": \"7:30-17:00\", \"Wednesday\": \"7:30-17:00\", \"Monday\": \"7:30-17:00\"}}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "# start_data_dir = '.'\n",
    "start_data_dir = '/Users/aliosha/Development/nlp/'\n",
    "\n",
    "data_directory = os.path.join(start_data_dir, 'data', 'yelp_dataset')\n",
    "\n",
    "businesses_filepath = os.path.join(data_directory, 'business.json')\n",
    "\n",
    "with open(businesses_filepath, encoding='utf_8') as f:\n",
    "    first_business_record = f.readline() \n",
    "\n",
    "print(first_business_record)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The business records consist of _key, value_ pairs containing information about the particular business. A few attributes we'll be interested in for this demo include:\n",
    "- __business\\_id__ &mdash; _unique identifier for businesses_\n",
    "- __categories__ &mdash; _an array containing relevant category values of businesses_\n",
    "\n",
    "The _categories_ attribute is of special interest. This demo will focus on restaurants, which are indicated by the presence of the _Restaurant_ tag in the _categories_ array. In addition, the _categories_ array may contain more detailed information about restaurants, such as the type of food they serve."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\"review_id\":\"v0i_UHJMo_hPBq9bxWvW4w\",\"user_id\":\"bv2nCi5Qv5vroFiqKGopiw\",\"business_id\":\"0W4lkclzZThpx3V65bVgig\",\"stars\":5,\"date\":\"2016-05-28\",\"text\":\"Love the staff, love the meat, love the place. Prepare for a long line around lunch or dinner hours. \\n\\nThey ask you how you want you meat, lean or something maybe, I can't remember. Just say you don't want it too fatty. \\n\\nGet a half sour pickle and a hot pepper. Hand cut french fries too.\",\"useful\":0,\"funny\":0,\"cool\":0}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "review_json_filepath = os.path.join(data_directory, 'review.json')\n",
    "\n",
    "with open(review_json_filepath, encoding='utf_8') as f:\n",
    "    first_review_record = f.readline()\n",
    "    \n",
    "print(first_review_record)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A few attributes of note on the review records:\n",
    "- __business\\_id__ &mdash; _indicates which business the review is about_\n",
    "- __text__ &mdash; _the natural language text the user wrote_\n",
    "\n",
    "The _text_ attribute will be our focus today!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "54,618 restaurants in the dataset.\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "\n",
    "restaurant_ids = set()\n",
    "\n",
    "# open the businesses file\n",
    "with open(businesses_filepath, encoding='utf_8') as f:\n",
    "    \n",
    "    # iterate through each line (json record) in the file\n",
    "    for business_json in f:\n",
    "        \n",
    "        # convert the json record to a Python dict\n",
    "        business = json.loads(business_json)\n",
    "        \n",
    "        # if this business is not a restaurant, skip to the next one\n",
    "        if u'Restaurants' not in business[u'categories']:\n",
    "            continue\n",
    "            \n",
    "        # add the restaurant business id to our restaurant_ids set\n",
    "        restaurant_ids.add(business[u'business_id'])\n",
    "\n",
    "# turn restaurant_ids into a frozenset, as we don't need to change it anymore\n",
    "restaurant_ids = frozenset(restaurant_ids)\n",
    "\n",
    "# print the number of unique restaurant ids in the dataset\n",
    "print('{:,}'.format(len(restaurant_ids)), u'restaurants in the dataset.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "intermediate_directory = os.path.join(data_directory, 'intermediate')\n",
    "\n",
    "review_txt_filepath = os.path.join(intermediate_directory,\n",
    "                                   'review_text_all.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.path.exists(intermediate_directory):\n",
    "    os.makedirs(intermediate_directory)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(review_txt_filepath, 'w+', encoding='utf_8') as review_txt_file:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Text from 1 restaurant reviews in the txt file.\n",
      "CPU times: user 159 µs, sys: 81 µs, total: 240 µs\n",
      "Wall time: 211 µs\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# this is a bit time consuming - make the if statement True\n",
    "# if you want to execute data prep yourself.\n",
    "max_rev = 10000\n",
    "process = True\n",
    "process = False\n",
    "\n",
    "review_count = 0\n",
    "\n",
    "\n",
    "if process:\n",
    "    # create & open a new file in write mode\n",
    "    with open(review_txt_filepath, 'w+', encoding='utf_8') as review_txt_file:\n",
    "\n",
    "        # open the existing review json file\n",
    "        with open(review_json_filepath, encoding='utf_8') as review_json_file:\n",
    "\n",
    "            # loop through all reviews in the existing file and convert to dict\n",
    "            for review_json in review_json_file:\n",
    "                review = json.loads(review_json)\n",
    "\n",
    "                # if this review is not about a restaurant, skip to the next one\n",
    "                if review[u'business_id'] not in restaurant_ids:\n",
    "                    continue\n",
    "\n",
    "                # write the restaurant review as a line in the new file\n",
    "                # escape newline characters in the original review text\n",
    "                review_txt_file.write(review[u'text'].replace('\\n', '\\\\n') + '\\n')\n",
    "                review_count += 1\n",
    "                if review_count >= max_rev:\n",
    "                    break\n",
    "\n",
    "    print(u'''Text from {:,} restaurant reviews\n",
    "              written to the new txt file.'''.format(review_count))\n",
    "    \n",
    "else:\n",
    "    with open(review_txt_filepath, encoding='utf_8') as review_txt_file:\n",
    "        for review_count, line in enumerate(review_txt_file):\n",
    "            pass\n",
    "        \n",
    "    print(u'Text from {:,} restaurant reviews in the txt file.'.format(review_count + 1))"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "import spacy\n",
    "import pandas as pd\n",
    "import itertools as it\n",
    "\n",
    "nlp = spacy.load('en')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I awake, wild eyed in a cold sweat, visions of caramel creme macaroons soliciting me like high class tricks in a whorehouse, Croque Monsieurs their swanky pimps, demanding payment for service. Essence strikes again. The only way to vanquish these dreams is to lunch here on a regular basis. \n",
      "\n",
      "I've been here twice in the past week alone and I think I'm addicted. The atmosphere, product, service, and baked goods have seduced me with their simple elegance. The only gripe I possibly have about this place is the fact that the parking lot is the size of a matchbox, and with Sacks, Cafe Lalibella and Starbucks all occupying the same plaza, finding a place to park during the busy lunch rush is like simultaneously playing Tetris and Frogger.\n",
      "\n",
      "The atmosphere at Essence is understated in its sophistication. Windows line the majority of the dining room, creating an inviting, open air feeling and the attached patio is charming. Strong earth toned colors and cute little origami flower bouquets grace the tables. Despite the small size of the establishment there are a variety of seating options for single patrons or small groups, and I've never seen people waiting for a table, even during their busiest Friday lunch rush.\n",
      "\n",
      "A broad array of patrons frequent Essence including hipsters, young professionals, older adults in their power suits, and even a few elders. I haven't seen very many college kids, which may because the food isn't cheap and it's probably \"weirder\" than options on Mill's strip. But I dig the \"weird\" French inspired cuisine. It's decadent and I feel naughty eating it.\n",
      "\n",
      "Everything I've eaten during lunch has been exceptional. The croque moniseur is far and away the best thing I've tried so far. A grilled sandwich with an egg washed top layer, thin sliced ham, creamy gruyere cheese and jalapenos. It's heavenly crunchy, just as the name suggests. The open face steak sandwich is tender delicious and smothered in buttery sauteed mushrooms with a sprinkle of bleu cheese crumbles as a savory accent. The layers of phyllo dough used in the spanakopita demonstrates some serious painstaking dedication to producing a good product, and the chopped Greek salad was phenomenal. Crispy cucumbers, bell peppers, kalmata olives, feta cheese and tomatoes that tasted like, well, actual tomatoes, for once. Something that you don't often find and an indication that Essence uses amazingly fresh organic product whenever possible. Even the side of potato salad with whole grain mustard was exceptional.\n",
      "\n",
      "The offerings at their bakery are just as phenomenal as their lunch entrees. The caramel creme macaroon is seriously the most amazing sandwich cookie I've ever tasted, a perfect harmony between savory creme filling and sweet chewy wafers. The strawberry shortcake is amazingly fresh and light, if not a little hard to eat. And their white chocolate raspberry layer cake is the classiest Little Debbie-esque snack cake I've ever had the pleasure of tasting. One day I vow to pick up a box of \"Tasty little treats\" just so I can sample a cupcake and about three cookies without having to choose among all the desserts on display!\n",
      "\n",
      "I highly recommend this place. Just don't be surprised if you develop an insatiable desire to consume Essence on a daily basis.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "with open(review_txt_filepath, encoding='utf_8') as f:\n",
    "    sample_review = list(it.islice(f, 340, 341))[0]\n",
    "    sample_review = sample_review.replace('\\\\n', '\\n')\n",
    "        \n",
    "print(sample_review)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 208 ms, sys: 40 ms, total: 248 ms\n",
      "Wall time: 231 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "parsed_review = nlp(sample_review)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I awake, wild eyed in a cold sweat, visions of caramel creme macaroons soliciting me like high class tricks in a whorehouse, Croque Monsieurs their swanky pimps, demanding payment for service. Essence strikes again. The only way to vanquish these dreams is to lunch here on a regular basis. \n",
      "\n",
      "I've been here twice in the past week alone and I think I'm addicted. The atmosphere, product, service, and baked goods have seduced me with their simple elegance. The only gripe I possibly have about this place is the fact that the parking lot is the size of a matchbox, and with Sacks, Cafe Lalibella and Starbucks all occupying the same plaza, finding a place to park during the busy lunch rush is like simultaneously playing Tetris and Frogger.\n",
      "\n",
      "The atmosphere at Essence is understated in its sophistication. Windows line the majority of the dining room, creating an inviting, open air feeling and the attached patio is charming. Strong earth toned colors and cute little origami flower bouquets grace the tables. Despite the small size of the establishment there are a variety of seating options for single patrons or small groups, and I've never seen people waiting for a table, even during their busiest Friday lunch rush.\n",
      "\n",
      "A broad array of patrons frequent Essence including hipsters, young professionals, older adults in their power suits, and even a few elders. I haven't seen very many college kids, which may because the food isn't cheap and it's probably \"weirder\" than options on Mill's strip. But I dig the \"weird\" French inspired cuisine. It's decadent and I feel naughty eating it.\n",
      "\n",
      "Everything I've eaten during lunch has been exceptional. The croque moniseur is far and away the best thing I've tried so far. A grilled sandwich with an egg washed top layer, thin sliced ham, creamy gruyere cheese and jalapenos. It's heavenly crunchy, just as the name suggests. The open face steak sandwich is tender delicious and smothered in buttery sauteed mushrooms with a sprinkle of bleu cheese crumbles as a savory accent. The layers of phyllo dough used in the spanakopita demonstrates some serious painstaking dedication to producing a good product, and the chopped Greek salad was phenomenal. Crispy cucumbers, bell peppers, kalmata olives, feta cheese and tomatoes that tasted like, well, actual tomatoes, for once. Something that you don't often find and an indication that Essence uses amazingly fresh organic product whenever possible. Even the side of potato salad with whole grain mustard was exceptional.\n",
      "\n",
      "The offerings at their bakery are just as phenomenal as their lunch entrees. The caramel creme macaroon is seriously the most amazing sandwich cookie I've ever tasted, a perfect harmony between savory creme filling and sweet chewy wafers. The strawberry shortcake is amazingly fresh and light, if not a little hard to eat. And their white chocolate raspberry layer cake is the classiest Little Debbie-esque snack cake I've ever had the pleasure of tasting. One day I vow to pick up a box of \"Tasty little treats\" just so I can sample a cupcake and about three cookies without having to choose among all the desserts on display!\n",
      "\n",
      "I highly recommend this place. Just don't be surprised if you develop an insatiable desire to consume Essence on a daily basis.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(parsed_review)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sentence 1:\n",
      "I awake, wild eyed in a cold sweat, visions of caramel creme macaroons soliciting me like high class tricks in a whorehouse, Croque Monsieurs their swanky pimps, demanding payment for service.\n",
      "###\n",
      "Sentence 2:\n",
      "Essence strikes again.\n",
      "###\n",
      "Sentence 3:\n",
      "The only way to vanquish these dreams is to lunch here on a regular basis. \n",
      "\n",
      "\n",
      "###\n",
      "Sentence 4:\n",
      "I've been here twice in the past week alone\n",
      "###\n",
      "Sentence 5:\n",
      "and I think I'm addicted.\n",
      "###\n",
      "Sentence 6:\n",
      "The atmosphere, product, service, and baked goods have seduced me with their simple elegance.\n",
      "###\n",
      "Sentence 7:\n",
      "The only gripe I possibly have about this place is the fact that the parking lot is the size of a matchbox, and with Sacks, Cafe Lalibella and Starbucks\n",
      "###\n",
      "Sentence 8:\n",
      "all occupying the same plaza, finding a place to park during the busy lunch rush is like simultaneously playing Tetris and Frogger.\n",
      "\n",
      "\n",
      "###\n",
      "Sentence 9:\n",
      "The atmosphere at Essence is understated in its sophistication.\n",
      "###\n",
      "Sentence 10:\n",
      "Windows line the majority of the dining room, creating an inviting, open air feeling and the attached patio is charming.\n",
      "###\n",
      "Sentence 11:\n",
      "Strong earth toned colors and cute little origami flower bouquets grace the tables.\n",
      "###\n",
      "Sentence 12:\n",
      "Despite the small size of the establishment there are a variety of seating options for single patrons or small groups, and I've never seen people waiting for a table, even during their busiest Friday lunch rush.\n",
      "\n",
      "\n",
      "###\n",
      "Sentence 13:\n",
      "A broad array of patrons frequent Essence including hipsters, young professionals, older adults in their power suits, and even a few elders.\n",
      "###\n",
      "Sentence 14:\n",
      "I haven't seen very many college kids, which may because the food isn't cheap\n",
      "###\n",
      "Sentence 15:\n",
      "and it's probably \"weirder\" than options on Mill's strip.\n",
      "###\n",
      "Sentence 16:\n",
      "But I dig the \"weird\" French inspired cuisine.\n",
      "###\n",
      "Sentence 17:\n",
      "It's decadent and I feel naughty eating it.\n",
      "\n",
      "\n",
      "###\n",
      "Sentence 18:\n",
      "Everything I've eaten during lunch has been exceptional.\n",
      "###\n",
      "Sentence 19:\n",
      "The croque moniseur is far and away the best thing I've tried so far.\n",
      "###\n",
      "Sentence 20:\n",
      "A grilled sandwich with an egg washed top layer, thin sliced ham, creamy gruyere cheese and jalapenos.\n",
      "###\n",
      "Sentence 21:\n",
      "It's heavenly crunchy, just as the name suggests.\n",
      "###\n",
      "Sentence 22:\n",
      "The open face steak sandwich is tender delicious and smothered in buttery sauteed mushrooms with a sprinkle of bleu cheese crumbles as a savory accent.\n",
      "###\n",
      "Sentence 23:\n",
      "The layers of phyllo dough used in the spanakopita demonstrates some serious painstaking dedication to producing a good product, and the chopped Greek salad was phenomenal.\n",
      "###\n",
      "Sentence 24:\n",
      "Crispy cucumbers, bell peppers, kalmata olives, feta cheese and tomatoes that tasted like, well, actual tomatoes, for once.\n",
      "###\n",
      "Sentence 25:\n",
      "Something that you don't often find and an indication that Essence uses amazingly fresh organic product whenever possible.\n",
      "###\n",
      "Sentence 26:\n",
      "Even the side of potato salad with whole grain mustard was exceptional.\n",
      "\n",
      "\n",
      "###\n",
      "Sentence 27:\n",
      "The offerings at their bakery are just as phenomenal as their lunch entrees.\n",
      "###\n",
      "Sentence 28:\n",
      "The caramel creme macaroon is seriously the most amazing sandwich cookie I've ever tasted, a perfect harmony between savory creme filling and sweet chewy wafers.\n",
      "###\n",
      "Sentence 29:\n",
      "The strawberry shortcake is amazingly fresh and light, if not a little hard to eat.\n",
      "###\n",
      "Sentence 30:\n",
      "And their white chocolate raspberry layer cake is the classiest Little Debbie-esque snack cake I've ever had the pleasure of tasting.\n",
      "###\n",
      "Sentence 31:\n",
      "One day I vow to pick up a box of \"Tasty little treats\" just so I can sample a cupcake and about three cookies without having to choose among all the desserts on display!\n",
      "\n",
      "\n",
      "###\n",
      "Sentence 32:\n",
      "I highly recommend this place.\n",
      "###\n",
      "Sentence 33:\n",
      "Just don't be surprised if you develop an insatiable desire to consume Essence on a daily basis.\n",
      "\n",
      "###\n"
     ]
    }
   ],
   "source": [
    "for num, sentence in enumerate(parsed_review.sents):\n",
    "    print('Sentence {}:'.format(num + 1))\n",
    "    print(sentence)\n",
    "    print('###')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Entity 1: Croque Monsieurs - EVENT\n",
      "\n",
      "Entity 2: the past week - DATE\n",
      "\n",
      "Entity 3: Sacks - PRODUCT\n",
      "\n",
      "Entity 4: Cafe Lalibella - GPE\n",
      "\n",
      "Entity 5: Starbucks - ORG\n",
      "\n",
      "Entity 6: Tetris - PRODUCT\n",
      "\n",
      "Entity 7: Frogger - PRODUCT\n",
      "\n",
      "Entity 8: Essence - ORG\n",
      "\n",
      "Entity 9: Friday - DATE\n",
      "\n",
      "Entity 10: Essence - ORG\n",
      "\n",
      "Entity 11: Mill - PERSON\n",
      "\n",
      "Entity 12: French - NORP\n",
      "\n",
      "Entity 13: Greek - NORP\n",
      "\n",
      "Entity 14: Essence - ORG\n",
      "\n",
      "Entity 15: Little Debbie-esque - PERSON\n",
      "\n",
      "Entity 16: One - CARDINAL\n",
      "\n",
      "Entity 17: about three - CARDINAL\n",
      "\n",
      "Entity 18: Essence - ORG\n",
      "\n",
      "Entity 19: daily - DATE\n",
      "\n",
      "Entity 20: \n",
      " - GPE\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for num, entity in enumerate(parsed_review.ents):\n",
    "    print('Entity {}:'.format(num + 1), entity, '-', entity.label_)\n",
    "    print('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>token_text</th>\n",
       "      <th>part_of_speech</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>I</td>\n",
       "      <td>PRON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>awake</td>\n",
       "      <td>VERB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>,</td>\n",
       "      <td>PUNCT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>wild</td>\n",
       "      <td>ADJ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>eyed</td>\n",
       "      <td>VERB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>in</td>\n",
       "      <td>ADP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>a</td>\n",
       "      <td>DET</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>cold</td>\n",
       "      <td>ADJ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>sweat</td>\n",
       "      <td>NOUN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>,</td>\n",
       "      <td>PUNCT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>visions</td>\n",
       "      <td>NOUN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>of</td>\n",
       "      <td>ADP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>caramel</td>\n",
       "      <td>ADJ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>creme</td>\n",
       "      <td>NOUN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>macaroons</td>\n",
       "      <td>NOUN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>soliciting</td>\n",
       "      <td>VERB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>me</td>\n",
       "      <td>PRON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>like</td>\n",
       "      <td>ADP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>high</td>\n",
       "      <td>ADJ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>class</td>\n",
       "      <td>NOUN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>tricks</td>\n",
       "      <td>NOUN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>in</td>\n",
       "      <td>ADP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>a</td>\n",
       "      <td>DET</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>whorehouse</td>\n",
       "      <td>NOUN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>,</td>\n",
       "      <td>PUNCT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Croque</td>\n",
       "      <td>PROPN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Monsieurs</td>\n",
       "      <td>PROPN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>their</td>\n",
       "      <td>ADJ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>swanky</td>\n",
       "      <td>NOUN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>pimps</td>\n",
       "      <td>NOUN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>603</th>\n",
       "      <td>on</td>\n",
       "      <td>ADP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>604</th>\n",
       "      <td>display</td>\n",
       "      <td>NOUN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>605</th>\n",
       "      <td>!</td>\n",
       "      <td>PUNCT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>606</th>\n",
       "      <td>\\n\\n</td>\n",
       "      <td>SPACE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>607</th>\n",
       "      <td>I</td>\n",
       "      <td>PRON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>608</th>\n",
       "      <td>highly</td>\n",
       "      <td>ADV</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>609</th>\n",
       "      <td>recommend</td>\n",
       "      <td>VERB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>610</th>\n",
       "      <td>this</td>\n",
       "      <td>DET</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>611</th>\n",
       "      <td>place</td>\n",
       "      <td>NOUN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>612</th>\n",
       "      <td>.</td>\n",
       "      <td>PUNCT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>613</th>\n",
       "      <td>Just</td>\n",
       "      <td>ADV</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>614</th>\n",
       "      <td>do</td>\n",
       "      <td>VERB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>615</th>\n",
       "      <td>n't</td>\n",
       "      <td>ADV</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>616</th>\n",
       "      <td>be</td>\n",
       "      <td>VERB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>617</th>\n",
       "      <td>surprised</td>\n",
       "      <td>ADJ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>618</th>\n",
       "      <td>if</td>\n",
       "      <td>ADP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>619</th>\n",
       "      <td>you</td>\n",
       "      <td>PRON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>620</th>\n",
       "      <td>develop</td>\n",
       "      <td>VERB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>621</th>\n",
       "      <td>an</td>\n",
       "      <td>DET</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>622</th>\n",
       "      <td>insatiable</td>\n",
       "      <td>ADJ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>623</th>\n",
       "      <td>desire</td>\n",
       "      <td>NOUN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>624</th>\n",
       "      <td>to</td>\n",
       "      <td>PART</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>625</th>\n",
       "      <td>consume</td>\n",
       "      <td>VERB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>626</th>\n",
       "      <td>Essence</td>\n",
       "      <td>PROPN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>627</th>\n",
       "      <td>on</td>\n",
       "      <td>ADP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>628</th>\n",
       "      <td>a</td>\n",
       "      <td>DET</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>629</th>\n",
       "      <td>daily</td>\n",
       "      <td>ADJ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>630</th>\n",
       "      <td>basis</td>\n",
       "      <td>NOUN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>631</th>\n",
       "      <td>.</td>\n",
       "      <td>PUNCT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>632</th>\n",
       "      <td>\\n</td>\n",
       "      <td>SPACE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>633 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     token_text part_of_speech\n",
       "0             I           PRON\n",
       "1         awake           VERB\n",
       "2             ,          PUNCT\n",
       "3          wild            ADJ\n",
       "4          eyed           VERB\n",
       "5            in            ADP\n",
       "6             a            DET\n",
       "7          cold            ADJ\n",
       "8         sweat           NOUN\n",
       "9             ,          PUNCT\n",
       "10      visions           NOUN\n",
       "11           of            ADP\n",
       "12      caramel            ADJ\n",
       "13        creme           NOUN\n",
       "14    macaroons           NOUN\n",
       "15   soliciting           VERB\n",
       "16           me           PRON\n",
       "17         like            ADP\n",
       "18         high            ADJ\n",
       "19        class           NOUN\n",
       "20       tricks           NOUN\n",
       "21           in            ADP\n",
       "22            a            DET\n",
       "23   whorehouse           NOUN\n",
       "24            ,          PUNCT\n",
       "25       Croque          PROPN\n",
       "26    Monsieurs          PROPN\n",
       "27        their            ADJ\n",
       "28       swanky           NOUN\n",
       "29        pimps           NOUN\n",
       "..          ...            ...\n",
       "603          on            ADP\n",
       "604     display           NOUN\n",
       "605           !          PUNCT\n",
       "606        \\n\\n          SPACE\n",
       "607           I           PRON\n",
       "608      highly            ADV\n",
       "609   recommend           VERB\n",
       "610        this            DET\n",
       "611       place           NOUN\n",
       "612           .          PUNCT\n",
       "613        Just            ADV\n",
       "614          do           VERB\n",
       "615         n't            ADV\n",
       "616          be           VERB\n",
       "617   surprised            ADJ\n",
       "618          if            ADP\n",
       "619         you           PRON\n",
       "620     develop           VERB\n",
       "621          an            DET\n",
       "622  insatiable            ADJ\n",
       "623      desire           NOUN\n",
       "624          to           PART\n",
       "625     consume           VERB\n",
       "626     Essence          PROPN\n",
       "627          on            ADP\n",
       "628           a            DET\n",
       "629       daily            ADJ\n",
       "630       basis           NOUN\n",
       "631           .          PUNCT\n",
       "632          \\n          SPACE\n",
       "\n",
       "[633 rows x 2 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "token_text = [token.orth_ for token in parsed_review]\n",
    "token_pos = [token.pos_ for token in parsed_review]\n",
    "\n",
    "pd.DataFrame(list(zip(token_text, token_pos)),\n",
    "             columns=['token_text', 'part_of_speech'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>token_text</th>\n",
       "      <th>token_lemma</th>\n",
       "      <th>token_shape</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>I</td>\n",
       "      <td>-PRON-</td>\n",
       "      <td>X</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>awake</td>\n",
       "      <td>awake</td>\n",
       "      <td>xxxx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>,</td>\n",
       "      <td>,</td>\n",
       "      <td>,</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>wild</td>\n",
       "      <td>wild</td>\n",
       "      <td>xxxx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>eyed</td>\n",
       "      <td>eye</td>\n",
       "      <td>xxxx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>in</td>\n",
       "      <td>in</td>\n",
       "      <td>xx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>a</td>\n",
       "      <td>a</td>\n",
       "      <td>x</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>cold</td>\n",
       "      <td>cold</td>\n",
       "      <td>xxxx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>sweat</td>\n",
       "      <td>sweat</td>\n",
       "      <td>xxxx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>,</td>\n",
       "      <td>,</td>\n",
       "      <td>,</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>visions</td>\n",
       "      <td>vision</td>\n",
       "      <td>xxxx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>of</td>\n",
       "      <td>of</td>\n",
       "      <td>xx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>caramel</td>\n",
       "      <td>caramel</td>\n",
       "      <td>xxxx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>creme</td>\n",
       "      <td>creme</td>\n",
       "      <td>xxxx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>macaroons</td>\n",
       "      <td>macaroon</td>\n",
       "      <td>xxxx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>soliciting</td>\n",
       "      <td>solicit</td>\n",
       "      <td>xxxx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>me</td>\n",
       "      <td>-PRON-</td>\n",
       "      <td>xx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>like</td>\n",
       "      <td>like</td>\n",
       "      <td>xxxx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>high</td>\n",
       "      <td>high</td>\n",
       "      <td>xxxx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>class</td>\n",
       "      <td>class</td>\n",
       "      <td>xxxx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>tricks</td>\n",
       "      <td>trick</td>\n",
       "      <td>xxxx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>in</td>\n",
       "      <td>in</td>\n",
       "      <td>xx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>a</td>\n",
       "      <td>a</td>\n",
       "      <td>x</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>whorehouse</td>\n",
       "      <td>whorehouse</td>\n",
       "      <td>xxxx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>,</td>\n",
       "      <td>,</td>\n",
       "      <td>,</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Croque</td>\n",
       "      <td>croque</td>\n",
       "      <td>Xxxxx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Monsieurs</td>\n",
       "      <td>monsieurs</td>\n",
       "      <td>Xxxxx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>their</td>\n",
       "      <td>-PRON-</td>\n",
       "      <td>xxxx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>swanky</td>\n",
       "      <td>swanky</td>\n",
       "      <td>xxxx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>pimps</td>\n",
       "      <td>pimp</td>\n",
       "      <td>xxxx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>603</th>\n",
       "      <td>on</td>\n",
       "      <td>on</td>\n",
       "      <td>xx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>604</th>\n",
       "      <td>display</td>\n",
       "      <td>display</td>\n",
       "      <td>xxxx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>605</th>\n",
       "      <td>!</td>\n",
       "      <td>!</td>\n",
       "      <td>!</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>606</th>\n",
       "      <td>\\n\\n</td>\n",
       "      <td>\\n\\n</td>\n",
       "      <td>\\n\\n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>607</th>\n",
       "      <td>I</td>\n",
       "      <td>-PRON-</td>\n",
       "      <td>X</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>608</th>\n",
       "      <td>highly</td>\n",
       "      <td>highly</td>\n",
       "      <td>xxxx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>609</th>\n",
       "      <td>recommend</td>\n",
       "      <td>recommend</td>\n",
       "      <td>xxxx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>610</th>\n",
       "      <td>this</td>\n",
       "      <td>this</td>\n",
       "      <td>xxxx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>611</th>\n",
       "      <td>place</td>\n",
       "      <td>place</td>\n",
       "      <td>xxxx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>612</th>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>613</th>\n",
       "      <td>Just</td>\n",
       "      <td>just</td>\n",
       "      <td>Xxxx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>614</th>\n",
       "      <td>do</td>\n",
       "      <td>do</td>\n",
       "      <td>xx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>615</th>\n",
       "      <td>n't</td>\n",
       "      <td>not</td>\n",
       "      <td>x'x</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>616</th>\n",
       "      <td>be</td>\n",
       "      <td>be</td>\n",
       "      <td>xx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>617</th>\n",
       "      <td>surprised</td>\n",
       "      <td>surprised</td>\n",
       "      <td>xxxx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>618</th>\n",
       "      <td>if</td>\n",
       "      <td>if</td>\n",
       "      <td>xx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>619</th>\n",
       "      <td>you</td>\n",
       "      <td>-PRON-</td>\n",
       "      <td>xxx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>620</th>\n",
       "      <td>develop</td>\n",
       "      <td>develop</td>\n",
       "      <td>xxxx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>621</th>\n",
       "      <td>an</td>\n",
       "      <td>an</td>\n",
       "      <td>xx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>622</th>\n",
       "      <td>insatiable</td>\n",
       "      <td>insatiable</td>\n",
       "      <td>xxxx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>623</th>\n",
       "      <td>desire</td>\n",
       "      <td>desire</td>\n",
       "      <td>xxxx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>624</th>\n",
       "      <td>to</td>\n",
       "      <td>to</td>\n",
       "      <td>xx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>625</th>\n",
       "      <td>consume</td>\n",
       "      <td>consume</td>\n",
       "      <td>xxxx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>626</th>\n",
       "      <td>Essence</td>\n",
       "      <td>essence</td>\n",
       "      <td>Xxxxx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>627</th>\n",
       "      <td>on</td>\n",
       "      <td>on</td>\n",
       "      <td>xx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>628</th>\n",
       "      <td>a</td>\n",
       "      <td>a</td>\n",
       "      <td>x</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>629</th>\n",
       "      <td>daily</td>\n",
       "      <td>daily</td>\n",
       "      <td>xxxx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>630</th>\n",
       "      <td>basis</td>\n",
       "      <td>basis</td>\n",
       "      <td>xxxx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>631</th>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>632</th>\n",
       "      <td>\\n</td>\n",
       "      <td>\\n</td>\n",
       "      <td>\\n</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>633 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     token_text token_lemma token_shape\n",
       "0             I      -PRON-           X\n",
       "1         awake       awake        xxxx\n",
       "2             ,           ,           ,\n",
       "3          wild        wild        xxxx\n",
       "4          eyed         eye        xxxx\n",
       "5            in          in          xx\n",
       "6             a           a           x\n",
       "7          cold        cold        xxxx\n",
       "8         sweat       sweat        xxxx\n",
       "9             ,           ,           ,\n",
       "10      visions      vision        xxxx\n",
       "11           of          of          xx\n",
       "12      caramel     caramel        xxxx\n",
       "13        creme       creme        xxxx\n",
       "14    macaroons    macaroon        xxxx\n",
       "15   soliciting     solicit        xxxx\n",
       "16           me      -PRON-          xx\n",
       "17         like        like        xxxx\n",
       "18         high        high        xxxx\n",
       "19        class       class        xxxx\n",
       "20       tricks       trick        xxxx\n",
       "21           in          in          xx\n",
       "22            a           a           x\n",
       "23   whorehouse  whorehouse        xxxx\n",
       "24            ,           ,           ,\n",
       "25       Croque      croque       Xxxxx\n",
       "26    Monsieurs   monsieurs       Xxxxx\n",
       "27        their      -PRON-        xxxx\n",
       "28       swanky      swanky        xxxx\n",
       "29        pimps        pimp        xxxx\n",
       "..          ...         ...         ...\n",
       "603          on          on          xx\n",
       "604     display     display        xxxx\n",
       "605           !           !           !\n",
       "606        \\n\\n        \\n\\n        \\n\\n\n",
       "607           I      -PRON-           X\n",
       "608      highly      highly        xxxx\n",
       "609   recommend   recommend        xxxx\n",
       "610        this        this        xxxx\n",
       "611       place       place        xxxx\n",
       "612           .           .           .\n",
       "613        Just        just        Xxxx\n",
       "614          do          do          xx\n",
       "615         n't         not         x'x\n",
       "616          be          be          xx\n",
       "617   surprised   surprised        xxxx\n",
       "618          if          if          xx\n",
       "619         you      -PRON-         xxx\n",
       "620     develop     develop        xxxx\n",
       "621          an          an          xx\n",
       "622  insatiable  insatiable        xxxx\n",
       "623      desire      desire        xxxx\n",
       "624          to          to          xx\n",
       "625     consume     consume        xxxx\n",
       "626     Essence     essence       Xxxxx\n",
       "627          on          on          xx\n",
       "628           a           a           x\n",
       "629       daily       daily        xxxx\n",
       "630       basis       basis        xxxx\n",
       "631           .           .           .\n",
       "632          \\n          \\n          \\n\n",
       "\n",
       "[633 rows x 3 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "token_lemma = [token.lemma_ for token in parsed_review]\n",
    "token_shape = [token.shape_ for token in parsed_review]\n",
    "\n",
    "pd.DataFrame(list(zip(token_text, token_lemma, token_shape)),\n",
    "             columns=['token_text', 'token_lemma', 'token_shape'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>token_text</th>\n",
       "      <th>entity_type</th>\n",
       "      <th>inside_outside_begin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>I</td>\n",
       "      <td></td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>awake</td>\n",
       "      <td></td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>,</td>\n",
       "      <td></td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>wild</td>\n",
       "      <td></td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>eyed</td>\n",
       "      <td></td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>in</td>\n",
       "      <td></td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>a</td>\n",
       "      <td></td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>cold</td>\n",
       "      <td></td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>sweat</td>\n",
       "      <td></td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>,</td>\n",
       "      <td></td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>visions</td>\n",
       "      <td></td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>of</td>\n",
       "      <td></td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>caramel</td>\n",
       "      <td></td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>creme</td>\n",
       "      <td></td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>macaroons</td>\n",
       "      <td></td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>soliciting</td>\n",
       "      <td></td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>me</td>\n",
       "      <td></td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>like</td>\n",
       "      <td></td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>high</td>\n",
       "      <td></td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>class</td>\n",
       "      <td></td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>tricks</td>\n",
       "      <td></td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>in</td>\n",
       "      <td></td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>a</td>\n",
       "      <td></td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>whorehouse</td>\n",
       "      <td></td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>,</td>\n",
       "      <td></td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Croque</td>\n",
       "      <td>EVENT</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Monsieurs</td>\n",
       "      <td>EVENT</td>\n",
       "      <td>I</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>their</td>\n",
       "      <td></td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>swanky</td>\n",
       "      <td></td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>pimps</td>\n",
       "      <td></td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>603</th>\n",
       "      <td>on</td>\n",
       "      <td></td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>604</th>\n",
       "      <td>display</td>\n",
       "      <td></td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>605</th>\n",
       "      <td>!</td>\n",
       "      <td></td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>606</th>\n",
       "      <td>\\n\\n</td>\n",
       "      <td></td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>607</th>\n",
       "      <td>I</td>\n",
       "      <td></td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>608</th>\n",
       "      <td>highly</td>\n",
       "      <td></td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>609</th>\n",
       "      <td>recommend</td>\n",
       "      <td></td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>610</th>\n",
       "      <td>this</td>\n",
       "      <td></td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>611</th>\n",
       "      <td>place</td>\n",
       "      <td></td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>612</th>\n",
       "      <td>.</td>\n",
       "      <td></td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>613</th>\n",
       "      <td>Just</td>\n",
       "      <td></td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>614</th>\n",
       "      <td>do</td>\n",
       "      <td></td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>615</th>\n",
       "      <td>n't</td>\n",
       "      <td></td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>616</th>\n",
       "      <td>be</td>\n",
       "      <td></td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>617</th>\n",
       "      <td>surprised</td>\n",
       "      <td></td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>618</th>\n",
       "      <td>if</td>\n",
       "      <td></td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>619</th>\n",
       "      <td>you</td>\n",
       "      <td></td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>620</th>\n",
       "      <td>develop</td>\n",
       "      <td></td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>621</th>\n",
       "      <td>an</td>\n",
       "      <td></td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>622</th>\n",
       "      <td>insatiable</td>\n",
       "      <td></td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>623</th>\n",
       "      <td>desire</td>\n",
       "      <td></td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>624</th>\n",
       "      <td>to</td>\n",
       "      <td></td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>625</th>\n",
       "      <td>consume</td>\n",
       "      <td></td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>626</th>\n",
       "      <td>Essence</td>\n",
       "      <td>ORG</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>627</th>\n",
       "      <td>on</td>\n",
       "      <td></td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>628</th>\n",
       "      <td>a</td>\n",
       "      <td></td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>629</th>\n",
       "      <td>daily</td>\n",
       "      <td>DATE</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>630</th>\n",
       "      <td>basis</td>\n",
       "      <td></td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>631</th>\n",
       "      <td>.</td>\n",
       "      <td></td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>632</th>\n",
       "      <td>\\n</td>\n",
       "      <td>GPE</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>633 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     token_text entity_type inside_outside_begin\n",
       "0             I                                O\n",
       "1         awake                                O\n",
       "2             ,                                O\n",
       "3          wild                                O\n",
       "4          eyed                                O\n",
       "5            in                                O\n",
       "6             a                                O\n",
       "7          cold                                O\n",
       "8         sweat                                O\n",
       "9             ,                                O\n",
       "10      visions                                O\n",
       "11           of                                O\n",
       "12      caramel                                O\n",
       "13        creme                                O\n",
       "14    macaroons                                O\n",
       "15   soliciting                                O\n",
       "16           me                                O\n",
       "17         like                                O\n",
       "18         high                                O\n",
       "19        class                                O\n",
       "20       tricks                                O\n",
       "21           in                                O\n",
       "22            a                                O\n",
       "23   whorehouse                                O\n",
       "24            ,                                O\n",
       "25       Croque       EVENT                    B\n",
       "26    Monsieurs       EVENT                    I\n",
       "27        their                                O\n",
       "28       swanky                                O\n",
       "29        pimps                                O\n",
       "..          ...         ...                  ...\n",
       "603          on                                O\n",
       "604     display                                O\n",
       "605           !                                O\n",
       "606        \\n\\n                                O\n",
       "607           I                                O\n",
       "608      highly                                O\n",
       "609   recommend                                O\n",
       "610        this                                O\n",
       "611       place                                O\n",
       "612           .                                O\n",
       "613        Just                                O\n",
       "614          do                                O\n",
       "615         n't                                O\n",
       "616          be                                O\n",
       "617   surprised                                O\n",
       "618          if                                O\n",
       "619         you                                O\n",
       "620     develop                                O\n",
       "621          an                                O\n",
       "622  insatiable                                O\n",
       "623      desire                                O\n",
       "624          to                                O\n",
       "625     consume                                O\n",
       "626     Essence         ORG                    B\n",
       "627          on                                O\n",
       "628           a                                O\n",
       "629       daily        DATE                    B\n",
       "630       basis                                O\n",
       "631           .                                O\n",
       "632          \\n         GPE                    B\n",
       "\n",
       "[633 rows x 3 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "token_entity_type = [token.ent_type_ for token in parsed_review]\n",
    "token_entity_iob = [token.ent_iob_ for token in parsed_review]\n",
    "\n",
    "pd.DataFrame(list(zip(token_text, token_entity_type, token_entity_iob)),\n",
    "             columns=['token_text', 'entity_type', 'inside_outside_begin'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>stop?</th>\n",
       "      <th>punctuation?</th>\n",
       "      <th>whitespace?</th>\n",
       "      <th>number?</th>\n",
       "      <th>out of vocab.?</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>I</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>awake</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>,</td>\n",
       "      <td></td>\n",
       "      <td>Yes</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>wild</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>eyed</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>in</td>\n",
       "      <td>Yes</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>a</td>\n",
       "      <td>Yes</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>cold</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>sweat</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>,</td>\n",
       "      <td></td>\n",
       "      <td>Yes</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>visions</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>of</td>\n",
       "      <td>Yes</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>caramel</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>creme</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>macaroons</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>soliciting</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>me</td>\n",
       "      <td>Yes</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>like</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>high</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>class</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>tricks</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>in</td>\n",
       "      <td>Yes</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>a</td>\n",
       "      <td>Yes</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>whorehouse</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>,</td>\n",
       "      <td></td>\n",
       "      <td>Yes</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Croque</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Monsieurs</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>their</td>\n",
       "      <td>Yes</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>swanky</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>pimps</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>603</th>\n",
       "      <td>on</td>\n",
       "      <td>Yes</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>604</th>\n",
       "      <td>display</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>605</th>\n",
       "      <td>!</td>\n",
       "      <td></td>\n",
       "      <td>Yes</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>606</th>\n",
       "      <td>\\n\\n</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Yes</td>\n",
       "      <td></td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>607</th>\n",
       "      <td>I</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>608</th>\n",
       "      <td>highly</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>609</th>\n",
       "      <td>recommend</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>610</th>\n",
       "      <td>this</td>\n",
       "      <td>Yes</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>611</th>\n",
       "      <td>place</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>612</th>\n",
       "      <td>.</td>\n",
       "      <td></td>\n",
       "      <td>Yes</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>613</th>\n",
       "      <td>Just</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>614</th>\n",
       "      <td>do</td>\n",
       "      <td>Yes</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>615</th>\n",
       "      <td>n't</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>616</th>\n",
       "      <td>be</td>\n",
       "      <td>Yes</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>617</th>\n",
       "      <td>surprised</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>618</th>\n",
       "      <td>if</td>\n",
       "      <td>Yes</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>619</th>\n",
       "      <td>you</td>\n",
       "      <td>Yes</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>620</th>\n",
       "      <td>develop</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>621</th>\n",
       "      <td>an</td>\n",
       "      <td>Yes</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>622</th>\n",
       "      <td>insatiable</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>623</th>\n",
       "      <td>desire</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>624</th>\n",
       "      <td>to</td>\n",
       "      <td>Yes</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>625</th>\n",
       "      <td>consume</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>626</th>\n",
       "      <td>Essence</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>627</th>\n",
       "      <td>on</td>\n",
       "      <td>Yes</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>628</th>\n",
       "      <td>a</td>\n",
       "      <td>Yes</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>629</th>\n",
       "      <td>daily</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>630</th>\n",
       "      <td>basis</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>631</th>\n",
       "      <td>.</td>\n",
       "      <td></td>\n",
       "      <td>Yes</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>632</th>\n",
       "      <td>\\n</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Yes</td>\n",
       "      <td></td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>633 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           text stop? punctuation? whitespace? number? out of vocab.?\n",
       "0             I                                                   Yes\n",
       "1         awake                                                   Yes\n",
       "2             ,                Yes                                Yes\n",
       "3          wild                                                   Yes\n",
       "4          eyed                                                   Yes\n",
       "5            in   Yes                                             Yes\n",
       "6             a   Yes                                             Yes\n",
       "7          cold                                                   Yes\n",
       "8         sweat                                                   Yes\n",
       "9             ,                Yes                                Yes\n",
       "10      visions                                                   Yes\n",
       "11           of   Yes                                             Yes\n",
       "12      caramel                                                   Yes\n",
       "13        creme                                                   Yes\n",
       "14    macaroons                                                   Yes\n",
       "15   soliciting                                                   Yes\n",
       "16           me   Yes                                             Yes\n",
       "17         like                                                   Yes\n",
       "18         high                                                   Yes\n",
       "19        class                                                   Yes\n",
       "20       tricks                                                   Yes\n",
       "21           in   Yes                                             Yes\n",
       "22            a   Yes                                             Yes\n",
       "23   whorehouse                                                   Yes\n",
       "24            ,                Yes                                Yes\n",
       "25       Croque                                                   Yes\n",
       "26    Monsieurs                                                   Yes\n",
       "27        their   Yes                                             Yes\n",
       "28       swanky                                                   Yes\n",
       "29        pimps                                                   Yes\n",
       "..          ...   ...          ...         ...     ...            ...\n",
       "603          on   Yes                                             Yes\n",
       "604     display                                                   Yes\n",
       "605           !                Yes                                Yes\n",
       "606        \\n\\n                            Yes                    Yes\n",
       "607           I                                                   Yes\n",
       "608      highly                                                   Yes\n",
       "609   recommend                                                   Yes\n",
       "610        this   Yes                                             Yes\n",
       "611       place                                                   Yes\n",
       "612           .                Yes                                Yes\n",
       "613        Just                                                   Yes\n",
       "614          do   Yes                                             Yes\n",
       "615         n't                                                   Yes\n",
       "616          be   Yes                                             Yes\n",
       "617   surprised                                                   Yes\n",
       "618          if   Yes                                             Yes\n",
       "619         you   Yes                                             Yes\n",
       "620     develop                                                   Yes\n",
       "621          an   Yes                                             Yes\n",
       "622  insatiable                                                   Yes\n",
       "623      desire                                                   Yes\n",
       "624          to   Yes                                             Yes\n",
       "625     consume                                                   Yes\n",
       "626     Essence                                                   Yes\n",
       "627          on   Yes                                             Yes\n",
       "628           a   Yes                                             Yes\n",
       "629       daily                                                   Yes\n",
       "630       basis                                                   Yes\n",
       "631           .                Yes                                Yes\n",
       "632          \\n                            Yes                    Yes\n",
       "\n",
       "[633 rows x 6 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "token_attributes = [(token.orth_,\n",
    "#                      token.prob,\n",
    "                     token.is_stop,\n",
    "                     token.is_punct,\n",
    "                     token.is_space,\n",
    "                     token.like_num,\n",
    "                     token.is_oov)\n",
    "                    for token in parsed_review]\n",
    "\n",
    "df = pd.DataFrame(token_attributes,\n",
    "                  columns=['text',\n",
    "#                            'log_probability',\n",
    "                           'stop?',\n",
    "                           'punctuation?',\n",
    "                           'whitespace?',\n",
    "                           'number?',\n",
    "                           'out of vocab.?'])\n",
    "\n",
    "df.loc[:, 'stop?':'out of vocab.?'] = (df.loc[:, 'stop?':'out of vocab.?']\n",
    "                                       .applymap(lambda x: u'Yes' if x else u''))\n",
    "                                               \n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from gensim.models import Phrases\n",
    "from gensim.models.word2vec import LineSentence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def punct_space(token):\n",
    "    \"\"\"\n",
    "    helper function to eliminate tokens\n",
    "    that are pure punctuation or whitespace\n",
    "    \"\"\"\n",
    "    \n",
    "    return token.is_punct or token.is_space\n",
    "\n",
    "def line_review(filename, max=None):\n",
    "    \"\"\"\n",
    "    generator function to read in reviews from the file\n",
    "    and un-escape the original line breaks in the text\n",
    "    \"\"\"\n",
    "    \n",
    "\n",
    "    with open(filename, encoding='utf_8') as f:\n",
    "        for i, review in enumerate(f):\n",
    "            if not max or i<= max:\n",
    "                yield review.replace('\\\\n', '\\n')\n",
    "            \n",
    "def lemmatized_sentence_corpus(filename):\n",
    "    \"\"\"\n",
    "    generator function to use spaCy to parse reviews,\n",
    "    lemmatize the text, and yield sentences\n",
    "    \"\"\"\n",
    "    \n",
    "    for parsed_review in nlp.pipe(line_review(filename),\n",
    "                                  batch_size=10000, n_threads=4):\n",
    "        \n",
    "        for sent in parsed_review.sents:\n",
    "            yield u' '.join([token.lemma_ for token in sent\n",
    "                             if not punct_space(token)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "unigram_sentences_filepath = os.path.join(intermediate_directory,\n",
    "                                          'unigram_sentences_all.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 7min 47s, sys: 1min 57s, total: 9min 44s\n",
      "Wall time: 7min 30s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# this is a bit time consuming - make the if statement True\n",
    "# if you want to execute data prep yourself.\n",
    "if process:\n",
    "\n",
    "    with open(unigram_sentences_filepath, 'w', encoding='utf_8') as f:\n",
    "        for sentence in lemmatized_sentence_corpus(review_txt_filepath):\n",
    "            f.write(sentence + '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "unigram_sentences = LineSentence(unigram_sentences_filepath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "decide to give this place a try base on the review\n",
      "\n",
      "service be really slow on a thursday afternoon work day\n",
      "\n",
      "-PRON- take almost 30 min before -PRON- get -PRON- food\n",
      "\n",
      "definitely not the place to go during the work week\n",
      "\n",
      "order a pork bone soup which be just ok\n",
      "\n",
      "nothing special or memorable about -PRON-\n",
      "\n",
      "not sure if -PRON- would come back again\n",
      "\n",
      "this place be really good\n",
      "\n",
      "the good restaurant in the area\n",
      "\n",
      "the food there be really tasty especially the ahi tuna club sandwich lobster ravioli and the tandoori chicken flat bread\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for unigram_sentence in it.islice(unigram_sentences, 340, 350):\n",
    "    print(u' '.join(unigram_sentence))\n",
    "    print(u'')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "bigram_model_filepath = os.path.join(intermediate_directory, 'bigram_model_all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 5.44 s, sys: 168 ms, total: 5.61 s\n",
      "Wall time: 6.16 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# this is a bit time consuming - make the if statement True\n",
    "# if you want to execute modeling yourself.\n",
    "if process:\n",
    "\n",
    "    bigram_model = Phrases(unigram_sentences)\n",
    "\n",
    "    bigram_model.save(bigram_model_filepath)\n",
    "    \n",
    "# load the finished model from disk\n",
    "bigram_model = Phrases.load(bigram_model_filepath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "bigram_sentences_filepath = os.path.join(intermediate_directory,\n",
    "                                         'bigram_sentences_all.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/aliosha/Envs/nlp/lib/python3.6/site-packages/gensim/models/phrases.py:486: UserWarning: For a faster implementation, use the gensim.models.phrases.Phraser class\n",
      "  warnings.warn(\"For a faster implementation, use the gensim.models.phrases.Phraser class\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 11.3 s, sys: 178 ms, total: 11.5 s\n",
      "Wall time: 12.4 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# this is a bit time consuming - make the if statement True\n",
    "# if you want to execute data prep yourself.\n",
    "if process:\n",
    "\n",
    "    with open(bigram_sentences_filepath, 'w', encoding='utf_8') as f:\n",
    "        \n",
    "        for unigram_sentence in unigram_sentences:\n",
    "            \n",
    "            bigram_sentence = u' '.join(bigram_model[unigram_sentence])\n",
    "            \n",
    "            f.write(bigram_sentence + '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "bigram_sentences = LineSentence(bigram_sentences_filepath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "decide to give this_place a try base_on the review\n",
      "\n",
      "service be really slow on a thursday afternoon work day\n",
      "\n",
      "-PRON- take almost 30_min before -PRON- get -PRON- food\n",
      "\n",
      "definitely not the place to go during the work week\n",
      "\n",
      "order a pork bone soup which be just ok\n",
      "\n",
      "nothing_special or memorable about -PRON-\n",
      "\n",
      "not sure if -PRON- would come_back again\n",
      "\n",
      "this_place be really good\n",
      "\n",
      "the good restaurant in the area\n",
      "\n",
      "the food there be really tasty especially the ahi_tuna club sandwich lobster_ravioli and the tandoori_chicken flat_bread\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for bigram_sentence in it.islice(bigram_sentences, 340, 350):\n",
    "    print(u' '.join(bigram_sentence))\n",
    "    print(u'')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "trigram_model_filepath = os.path.join(intermediate_directory,\n",
    "                                      'trigram_model_all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 5.27 s, sys: 164 ms, total: 5.44 s\n",
      "Wall time: 5.9 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# this is a bit time consuming - make the if statement True\n",
    "# if you want to execute modeling yourself.\n",
    "if process:\n",
    "\n",
    "    trigram_model = Phrases(bigram_sentences)\n",
    "\n",
    "    trigram_model.save(trigram_model_filepath)\n",
    "    \n",
    "# load the finished model from disk\n",
    "trigram_model = Phrases.load(trigram_model_filepath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "trigram_sentences_filepath = os.path.join(intermediate_directory,\n",
    "                                          'trigram_sentences_all.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/aliosha/Envs/nlp/lib/python3.6/site-packages/gensim/models/phrases.py:486: UserWarning: For a faster implementation, use the gensim.models.phrases.Phraser class\n",
      "  warnings.warn(\"For a faster implementation, use the gensim.models.phrases.Phraser class\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 11.1 s, sys: 195 ms, total: 11.3 s\n",
      "Wall time: 12.2 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# this is a bit time consuming - make the if statement True\n",
    "# if you want to execute data prep yourself.\n",
    "if process:\n",
    "\n",
    "    with open(trigram_sentences_filepath, 'w', encoding='utf_8') as f:\n",
    "        \n",
    "        for bigram_sentence in bigram_sentences:\n",
    "            \n",
    "            trigram_sentence = u' '.join(trigram_model[bigram_sentence])\n",
    "            \n",
    "            f.write(trigram_sentence + '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for bigram_sentence in it.islice(tri, 340, 350):\n",
    "    print(u' '.join(bigram_sentence))\n",
    "    print(u'')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "trigram_sentences = LineSentence(trigram_sentences_filepath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "decide to give this_place a try base_on the review\n",
      "\n",
      "service be really slow on a thursday afternoon work day\n",
      "\n",
      "-PRON- take almost 30_min before -PRON- get -PRON- food\n",
      "\n",
      "definitely not the place to go during the work week\n",
      "\n",
      "order a pork bone soup which be just ok\n",
      "\n",
      "nothing_special or memorable about -PRON-\n",
      "\n",
      "not sure if -PRON- would come_back_again\n",
      "\n",
      "this_place be really good\n",
      "\n",
      "the good restaurant in the area\n",
      "\n",
      "the food there be really tasty especially the ahi_tuna club sandwich lobster_ravioli and the tandoori_chicken flat_bread\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for trigram_sentence in it.islice(trigram_sentences, 340, 350):\n",
    "    print(u' '.join(trigram_sentence))\n",
    "    print(u'')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5682\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Counter({'10_15_minute': 7,\n",
       "         '10_minute_before': 11,\n",
       "         '10_minute_later': 6,\n",
       "         '10_year_ago': 9,\n",
       "         '15_20_minute': 9,\n",
       "         '15_minute_before': 7,\n",
       "         '20_minute_later': 7,\n",
       "         '2_star_because': 10,\n",
       "         '2_year_old': 9,\n",
       "         '3_1/2_star': 6,\n",
       "         '45_minute_wait': 9,\n",
       "         '4_year_old': 7,\n",
       "         '5_10_minute': 8,\n",
       "         '5_year_old': 7,\n",
       "         '8_year_ago': 5,\n",
       "         'a___erto': 1,\n",
       "         'about_$_10': 20,\n",
       "         'about_10_minute': 23,\n",
       "         'about_15_minute': 22,\n",
       "         'about_20_minute': 27,\n",
       "         'about_five_minute': 9,\n",
       "         'about_ten_minute': 10,\n",
       "         'always_leave_satisfied': 6,\n",
       "         'an_add_bonus': 7,\n",
       "         'an_early_dinner': 12,\n",
       "         'an_empty_stomach': 7,\n",
       "         'an_enjoyable_experience': 6,\n",
       "         'an_extra_star': 8,\n",
       "         'an_hour_later': 6,\n",
       "         'an_old_house': 7,\n",
       "         'another_10_minute': 12,\n",
       "         'another_15_minute': 11,\n",
       "         'answer_any_question': 7,\n",
       "         'anything_less_than': 8,\n",
       "         'around_1_p.m.': 6,\n",
       "         'as_an_appetizer': 41,\n",
       "         'as_many_yelper': 8,\n",
       "         'as_soon_as': 84,\n",
       "         'as_well_as': 174,\n",
       "         'at_6_p.m.': 11,\n",
       "         'at_7_p.m.': 12,\n",
       "         'at_first_glance': 7,\n",
       "         'at_least_once': 39,\n",
       "         'at_least_twice': 9,\n",
       "         'attendez_vous_à': 6,\n",
       "         'au_gratin_potato': 7,\n",
       "         'authentic_mexican_food': 12,\n",
       "         'avoid_this_place': 18,\n",
       "         'back_anytime_soon': 3,\n",
       "         \"bad_daddy_'s\": 6,\n",
       "         'barely_any_meat': 7,\n",
       "         'base_on_yelp_review': 6,\n",
       "         'beef_noodle_soup': 13,\n",
       "         'beef_short_rib': 10,\n",
       "         'beer_on_tap': 26,\n",
       "         'big_fan_of': 76,\n",
       "         'bit_more_expensive': 8,\n",
       "         'bit_too_salty': 11,\n",
       "         'black_bean_burger': 9,\n",
       "         \"bloody_mary_'s\": 11,\n",
       "         'blow_away_by': 21,\n",
       "         'braise_short_rib': 11,\n",
       "         'buffalo_chicken_pizza': 7,\n",
       "         'buffalo_wild_wings': 9,\n",
       "         'bun_bo_hue': 10,\n",
       "         'cafe_au_lait': 6,\n",
       "         'capicola_or_pastrami': 6,\n",
       "         'carne_asada_burrito': 8,\n",
       "         'carne_asada_taco': 12,\n",
       "         'cast_iron_skillet': 6,\n",
       "         \"ce_n'_pas\": 6,\n",
       "         'ce_qui_est': 6,\n",
       "         'chicago_hot_dog': 6,\n",
       "         'chicken_pot_pie': 8,\n",
       "         'chicken_tikka_masala': 23,\n",
       "         'chilean_sea_bass': 11,\n",
       "         'chocolate_chip_cookie': 19,\n",
       "         'chocolate_cover_strawberry': 7,\n",
       "         'come_back_again': 44,\n",
       "         'corned_beef_hash': 9,\n",
       "         'couple_week_ago': 10,\n",
       "         'decent_beer_selection': 8,\n",
       "         'deep_dish_pizza': 13,\n",
       "         'definitely_come_back': 51,\n",
       "         'definitely_recommend_this_place': 8,\n",
       "         'definitely_worth_check_out': 6,\n",
       "         \"del_frisco_'s\": 9,\n",
       "         'depend_on_what': 13,\n",
       "         'deserve_5_star': 9,\n",
       "         'different_type_of': 20,\n",
       "         'do_not_bother': 35,\n",
       "         'do_not_care': 126,\n",
       "         'do_not_disappoint': 82,\n",
       "         'do_not_know': 259,\n",
       "         'do_not_know_how': 53,\n",
       "         'do_not_know_what': 79,\n",
       "         'do_not_matter': 17,\n",
       "         'do_not_mind': 87,\n",
       "         'do_not_skimp_on': 15,\n",
       "         'do_not_understand_why': 12,\n",
       "         'do_not_waste': 28,\n",
       "         'do_not_worry': 14,\n",
       "         'dobdden97_qwuq90g3fahq#dobdden97_qwuq90g3fahq.': 1,\n",
       "         'duck_fat_fry': 7,\n",
       "         'dulce_de_leche': 11,\n",
       "         'during_happy_hour': 39,\n",
       "         'during_lunch_hour': 6,\n",
       "         'during_lunch_time': 10,\n",
       "         'during_peak_hour': 8,\n",
       "         'early_bird_special': 7,\n",
       "         'eat_here_twice': 13,\n",
       "         'egg_drop_soup': 8,\n",
       "         'et_de_la': 6,\n",
       "         'every_single_time': 13,\n",
       "         \"famous_dave_'s\": 6,\n",
       "         'far_away_from': 7,\n",
       "         'fast_food_chain': 7,\n",
       "         'fast_food_joint': 22,\n",
       "         \"father_'s_day\": 8,\n",
       "         'few_day_ago': 9,\n",
       "         'few_month_ago': 7,\n",
       "         'few_week_ago': 17,\n",
       "         'few_year_ago': 14,\n",
       "         'figure_out_how': 8,\n",
       "         'figure_out_what': 13,\n",
       "         'first_time_yesterday': 8,\n",
       "         'five_minute_later': 4,\n",
       "         'five_star_rating': 12,\n",
       "         'five_star_review': 9,\n",
       "         'flourless_chocolate_cake': 7,\n",
       "         'for_lunch_today': 22,\n",
       "         'for_some_reason': 54,\n",
       "         'for_those_who': 61,\n",
       "         'french_onion_soup': 16,\n",
       "         'fried_green_tomato': 6,\n",
       "         \"friend_'s_birthday\": 20,\n",
       "         'from_san_francisco': 7,\n",
       "         'frozen_hot_chocolate': 8,\n",
       "         'fruit_de_mer': 14,\n",
       "         'fry_green_tomato': 9,\n",
       "         \"general_tso_'s\": 11,\n",
       "         'get_rid_of': 14,\n",
       "         'give_5_star': 22,\n",
       "         'gluten_free_crust': 8,\n",
       "         'go_back_again': 40,\n",
       "         'go_down_hill': 8,\n",
       "         'go_somewhere_else': 22,\n",
       "         'go_wrong_with': 20,\n",
       "         'green_tea_ice_cream': 12,\n",
       "         'greet_right_away': 8,\n",
       "         'grill_cheese_sandwich': 11,\n",
       "         'half_an_hour': 11,\n",
       "         'handful_of_time': 9,\n",
       "         'happy_hour_special': 21,\n",
       "         'hard_boil_egg': 7,\n",
       "         'have_no_idea': 45,\n",
       "         'here_several_time': 27,\n",
       "         'here_twice_now': 14,\n",
       "         'high_end_restaurant': 12,\n",
       "         'hot_n_juicy': 7,\n",
       "         'http://www.yelp.com/biz_photos/aliz%c3%a9-las-vegas-2#e44xnklzdovuioy_wnldqg': 1,\n",
       "         'http://www.yelp.com/biz_photos/aliz%c3%a9-las-vegas-2#efbilq_yx9w-mtz1ofe4lq': 1,\n",
       "         'http://www.yelp.com/biz_photos/aqab9ylddp_r9zgrdt7mfa?select=ajg1uop4c7q6b1cwbwj65g': 1,\n",
       "         'http://www.yelp.com/biz_photos/aqab9ylddp_r9zgrdt7mfa?select=exvvx1o49t64awto_zm0cq': 1,\n",
       "         'http://www.yelp.com/biz_photos/aqab9ylddp_r9zgrdt7mfa?select=g0sfpyrvp_eirj4kdg8chg': 2,\n",
       "         'http://www.yelp.com/biz_photos/ayetowsgi9vx0-xffrgjww?select=hk3bddmzc9xwsm7oy_05og': 1,\n",
       "         'http://www.yelp.com/biz_photos/er8dppwf_lhhv64ncwhd8g?selected=qa6sekzkxorekzk_gpx1cg#lsatyk2xsdwpwdbeet-wgw': 1,\n",
       "         'http://www.yelp.com/biz_photos/k0j_znzin0jshxvnpvw86a?select=dobdden97_qwuq90g3fahq#1qw63zi6jhtag-pmhu_nsg': 1,\n",
       "         'http://www.yelp.com/biz_photos/k0j_znzin0jshxvnpvw86a?select=dobdden97_qwuq90g3fahq#5novijuiihu5nbs2mgfhjw': 1,\n",
       "         'http://www.yelp.com/biz_photos/k0j_znzin0jshxvnpvw86a?select=dobdden97_qwuq90g3fahq#rcjvfsq828px9z1qgseu1g': 1,\n",
       "         'http://www.yelp.com/biz_photos/k0j_znzin0jshxvnpvw86a?select=dobdden97_qwuq90g3fahq#t0tizmlvjt3tf3ktro439g': 1,\n",
       "         'http://www.yelp.com/biz_photos/k0j_znzin0jshxvnpvw86a?select=dobdden97_qwuq90g3fahq#zfhsqwfmrz73uw3ejhsdcw': 1,\n",
       "         'http://www.yelp.com/biz_photos/l_zltfhvfzfonvq0-oktxg?select=b5aveyaln1b7a4omjvgoba': 1,\n",
       "         'http://www.yelp.com/biz_photos/l_zltfhvfzfonvq0-oktxg?select=ilyhtbvonwm-pgooo9gixq': 1,\n",
       "         'http://www.yelp.com/biz_photos/l_zltfhvfzfonvq0-oktxg?select=krlnmg-eqrib8smtt-rbxg': 1,\n",
       "         'http://www.yelp.com/biz_photos/l_zltfhvfzfonvq0-oktxg?select=tlnefca82jbtkc-lyek3hq': 1,\n",
       "         'http://www.yelp.com/biz_photos/l_zltfhvfzfonvq0-oktxg?select=unzdvica54dm08njtxomvg': 1,\n",
       "         'http://www.yelp.com/biz_photos/l_zltfhvfzfonvq0-oktxg?select=v8z1y4kymu0hty0_xc0ftw': 1,\n",
       "         'http://www.yelp.com/biz_photos/mudalytuatnzm2k9zoh27q?select=0wg3dw22rv_ucgluabhmqa': 1,\n",
       "         'http://www.yelp.com/biz_photos/mudalytuatnzm2k9zoh27q?select=rckqijcs-_n9rwujerb8sw': 1,\n",
       "         'http://www.yelp.com/biz_photos/mudalytuatnzm2k9zoh27q?select=vkmygyuvrs_6-osdiqb2aq': 1,\n",
       "         'http://www.yelp.com/biz_photos/mudalytuatnzm2k9zoh27q?select=zg_ppe50thejmxog2qhhrq': 1,\n",
       "         'http://www.yelp.com/biz_photos/otto-enoteca-pizzeria-las-vegas#6q7nonkvamfrk_jyyfyila': 1,\n",
       "         'http://www.yelp.com/biz_photos/rcapajgkojc2vo_l3xa42a?select=8q1x983xtrvtg4au3taorq': 1,\n",
       "         'http://www.yelp.com/biz_photos/rcapajgkojc2vo_l3xa42a?select=dmmyzhqfxn_rakglcnfiuw': 1,\n",
       "         'http://www.yelp.com/biz_photos/rcapajgkojc2vo_l3xa42a?select=lne84rjoww7lzotwn-cv6g': 1,\n",
       "         'http://www.yelp.com/biz_photos/rcapajgkojc2vo_l3xa42a?select=lwkweee4qlnbg8uvdyhs0q': 1,\n",
       "         'http://www.yelp.com/biz_photos/rcapajgkojc2vo_l3xa42a?select=xhqsvqwxpdz_kjl4s5qgiw': 1,\n",
       "         'http://www.yelp.com/biz_photos/xnjeadlrlj9azb8qsdir2q?select=aqnejmu_awq3zmpfnbippq': 1,\n",
       "         'http://www.yelp.com/biz_photos/xnjeadlrlj9azb8qsdir2q?select=ckyqx9bwior5x9uuw5l_3w': 1,\n",
       "         'http://www.yelp.com/biz_photos/xnjeadlrlj9azb8qsdir2q?select=eu_ahdxcsi_hr5p7t3pxvg': 1,\n",
       "         'http://www.yelp.com/biz_photos/xnjeadlrlj9azb8qsdir2q?select=kghh1krmlrewyewz0_4mhg': 1,\n",
       "         'http://www.yelp.com/biz_photos/xnjeadlrlj9azb8qsdir2q?select=lhws6rq1rse_qj2ttkb1lw': 1,\n",
       "         'http://www.yelp.com/biz_photos/xnjeadlrlj9azb8qsdir2q?select=pqqa7f1e_oligznuz7a9ow': 1,\n",
       "         'http://www.yelp.com/biz_photos/xnjeadlrlj9azb8qsdir2q?select=qyttbgiq_kz0xii7gkzdxq': 1,\n",
       "         'http://www.yelp.com/biz_photos/xnjeadlrlj9azb8qsdir2q?select=sf5cz0n1mx--u1r_-algmw': 1,\n",
       "         'huge_fan_of': 56,\n",
       "         'in_term_of': 73,\n",
       "         \"j'ai_bien_aimé\": 8,\n",
       "         'je_vous_conseille': 10,\n",
       "         \"jimmy_john_'s\": 6,\n",
       "         'keep_come_back': 27,\n",
       "         'kid_run_around': 6,\n",
       "         'know_exactly_what': 12,\n",
       "         'kung_pao_chicken': 9,\n",
       "         'las_vegas_blvd': 8,\n",
       "         'las_vegas_strip': 13,\n",
       "         'late_night_snack': 9,\n",
       "         'less_than_$_10': 7,\n",
       "         'less_than_10_minute': 9,\n",
       "         'little_hole_in': 10,\n",
       "         'look_forward_to': 164,\n",
       "         'loose_leaf_tea': 6,\n",
       "         'mac_n_cheese': 65,\n",
       "         'main_dining_area': 6,\n",
       "         'make_an_effort': 7,\n",
       "         'make_eye_contact': 13,\n",
       "         'make_from_scratch': 10,\n",
       "         'mall_food_court': 7,\n",
       "         'matzo_ball_soup': 9,\n",
       "         'mixed_feeling_about': 6,\n",
       "         'mixed_green_salad': 7,\n",
       "         'more_expensive_than': 17,\n",
       "         'more_often_than': 10,\n",
       "         'more_than_enough': 33,\n",
       "         'more_than_happy': 15,\n",
       "         'most_recent_visit': 17,\n",
       "         \"mother_'s_day\": 10,\n",
       "         'never_feel_rush': 6,\n",
       "         'new_york_new_york': 6,\n",
       "         'new_york_strip': 6,\n",
       "         'new_york_style': 9,\n",
       "         'no_big_deal': 15,\n",
       "         'no_idea_what': 1,\n",
       "         'no_matter_how': 15,\n",
       "         'no_matter_what': 11,\n",
       "         'not_anything_special': 20,\n",
       "         'not_crazy_about': 20,\n",
       "         'not_go_wrong': 72,\n",
       "         'not_overly_impressed': 12,\n",
       "         'not_sure_why': 30,\n",
       "         'not_too_heavy': 14,\n",
       "         'not_too_sweet': 50,\n",
       "         'nothing_stand_out': 9,\n",
       "         'oh_my_god': 10,\n",
       "         'oh_my_gosh': 8,\n",
       "         'old_town_scottsdale': 10,\n",
       "         'on_several_occasion': 14,\n",
       "         'open_24_hour': 12,\n",
       "         'over_$_20': 9,\n",
       "         'over_an_hour': 25,\n",
       "         'pair_well_with': 13,\n",
       "         'people_who_work': 10,\n",
       "         'perfect_medium_rare': 12,\n",
       "         \"pf_chang_'s\": 6,\n",
       "         'philly_cheese_steak': 12,\n",
       "         'pico_de_gallo': 25,\n",
       "         'pleasantly_surprise_by': 8,\n",
       "         'plenty_of_option': 12,\n",
       "         'plenty_of_seating': 18,\n",
       "         'plenty_of_space': 9,\n",
       "         'pretty_damn_good': 15,\n",
       "         'pull_pork_sandwich': 16,\n",
       "         'rave_about_this_place': 13,\n",
       "         'really_stand_out': 21,\n",
       "         'recommend_this_place': 62,\n",
       "         'red_velvet_cake': 8,\n",
       "         'reverse_happy_hour': 11,\n",
       "         'right_across_from': 9,\n",
       "         'right_amount_of': 75,\n",
       "         'right_next_door': 9,\n",
       "         'roasted_red_pepper': 8,\n",
       "         'run_out_of': 68,\n",
       "         \"ruth_'s_chris\": 8,\n",
       "         'saturday_night_around': 9,\n",
       "         'seat_right_away': 31,\n",
       "         'service_à_la': 12,\n",
       "         \"shepherd_'s_pie\": 10,\n",
       "         'shrimp_po_boy': 8,\n",
       "         'sister_in_law': 9,\n",
       "         'snow_crab_leg': 9,\n",
       "         'soft_shell_crab': 17,\n",
       "         'some_sort_of': 46,\n",
       "         'spicy_salmon_roll': 10,\n",
       "         'spicy_tuna_roll': 14,\n",
       "         'start_off_with': 53,\n",
       "         'stay_away_from': 15,\n",
       "         'steak_n_shake': 8,\n",
       "         'sticky_toffee_pudding': 9,\n",
       "         'strip_mall_next': 10,\n",
       "         'stumble_upon_this_place': 7,\n",
       "         'sun_dry_tomato': 8,\n",
       "         'sunny_side_up': 9,\n",
       "         'support_local_business': 7,\n",
       "         'sweet_chili_sauce': 7,\n",
       "         'sweet_potato_fry': 69,\n",
       "         'sweet_potato_tot': 8,\n",
       "         'tacos_el_gordo': 7,\n",
       "         'take_advantage_of': 24,\n",
       "         'take_care_of': 62,\n",
       "         'ten_minute_later': 5,\n",
       "         'texas_de_brazil': 9,\n",
       "         'thai_ice_tea': 10,\n",
       "         'thai_iced_tea': 6,\n",
       "         'that_being_say': 16,\n",
       "         'thick_cut_bacon': 6,\n",
       "         'thin_crust_pizza': 14,\n",
       "         'this_little_gem': 17,\n",
       "         'this_particular_location': 18,\n",
       "         'this_past_weekend': 13,\n",
       "         'tomato_basil_soup': 6,\n",
       "         'ton_of_tv': 7,\n",
       "         'too_far_away': 11,\n",
       "         'too_much_dressing': 9,\n",
       "         'tout_le_monde': 8,\n",
       "         'try_something_different': 23,\n",
       "         'try_something_else': 24,\n",
       "         'try_something_new': 26,\n",
       "         'tuck_away_in': 22,\n",
       "         'turn_off_by': 7,\n",
       "         'under_$_10': 11,\n",
       "         'under_$_20': 7,\n",
       "         \"valentine_'s_day\": 14,\n",
       "         'vanilla_ice_cream': 21,\n",
       "         'very_knowledgeable_about': 10,\n",
       "         'very_reasonable_price': 15,\n",
       "         'way_too_long': 10,\n",
       "         'way_too_much': 28,\n",
       "         'way_too_salty': 12,\n",
       "         'way_too_sweet': 8,\n",
       "         'well_take_care': 13,\n",
       "         'wide_variety_of': 21,\n",
       "         'will_definitely_come_back': 42,\n",
       "         'will_definitely_return': 44,\n",
       "         'within_5_minute': 7,\n",
       "         'wood_fire_pizza': 11,\n",
       "         'worth_check_out': 9,\n",
       "         'worth_every_penny': 14,\n",
       "         'would_definitely_recommend': 47,\n",
       "         'would_definitely_return': 10,\n",
       "         'write_home_about': 54,\n",
       "         'xiao_long_bao': 7,\n",
       "         \"year_'s_eve\": 7,\n",
       "         'year_old_daughter': 3,\n",
       "         'year_old_son': 8,\n",
       "         'yum_yum_yum': 11,\n",
       "         'à_la_clientèle': 1})"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import re\n",
    "from collections import Counter\n",
    "\n",
    "p = re.compile('.*_.*_.*')\n",
    "\n",
    "selected = []\n",
    "trigrams = Counter()\n",
    "\n",
    "for trigram_sentence_ext in trigram_sentences:\n",
    "    for word in trigram_sentence_ext:\n",
    "        if p.search(word):\n",
    "            selected.append(trigram_sentence_ext)\n",
    "            trigrams[word] += 1\n",
    "            \n",
    "        \n",
    "print(len(selected))\n",
    "trigrams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('do_not_know', 259),\n",
       " ('as_well_as', 174),\n",
       " ('look_forward_to', 164),\n",
       " ('do_not_care', 126),\n",
       " ('do_not_mind', 87),\n",
       " ('as_soon_as', 84),\n",
       " ('do_not_disappoint', 82),\n",
       " ('do_not_know_what', 79),\n",
       " ('big_fan_of', 76),\n",
       " ('right_amount_of', 75),\n",
       " ('in_term_of', 73),\n",
       " ('not_go_wrong', 72),\n",
       " ('sweet_potato_fry', 69),\n",
       " ('run_out_of', 68),\n",
       " ('mac_n_cheese', 65),\n",
       " ('recommend_this_place', 62),\n",
       " ('take_care_of', 62),\n",
       " ('for_those_who', 61),\n",
       " ('huge_fan_of', 56),\n",
       " ('for_some_reason', 54),\n",
       " ('write_home_about', 54),\n",
       " ('start_off_with', 53),\n",
       " ('do_not_know_how', 53),\n",
       " ('definitely_come_back', 51),\n",
       " ('not_too_sweet', 50),\n",
       " ('would_definitely_recommend', 47),\n",
       " ('some_sort_of', 46),\n",
       " ('have_no_idea', 45),\n",
       " ('come_back_again', 44),\n",
       " ('will_definitely_return', 44),\n",
       " ('will_definitely_come_back', 42),\n",
       " ('as_an_appetizer', 41),\n",
       " ('go_back_again', 40),\n",
       " ('during_happy_hour', 39),\n",
       " ('at_least_once', 39),\n",
       " ('do_not_bother', 35),\n",
       " ('more_than_enough', 33),\n",
       " ('seat_right_away', 31),\n",
       " ('not_sure_why', 30),\n",
       " ('do_not_waste', 28),\n",
       " ('way_too_much', 28),\n",
       " ('here_several_time', 27),\n",
       " ('about_20_minute', 27),\n",
       " ('keep_come_back', 27),\n",
       " ('try_something_new', 26),\n",
       " ('beer_on_tap', 26),\n",
       " ('over_an_hour', 25),\n",
       " ('pico_de_gallo', 25),\n",
       " ('try_something_else', 24),\n",
       " ('take_advantage_of', 24)]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trigrams.most_common(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "trigram_reviews_filepath = os.path.join(intermediate_directory,\n",
    "                                        'trigram_transformed_reviews_all.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/aliosha/Envs/nlp/lib/python3.6/site-packages/gensim/models/phrases.py:486: UserWarning: For a faster implementation, use the gensim.models.phrases.Phraser class\n",
      "  warnings.warn(\"For a faster implementation, use the gensim.models.phrases.Phraser class\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 8min 3s, sys: 2min, total: 10min 3s\n",
      "Wall time: 7min 46s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# this is a bit time consuming - make the if statement True\n",
    "# if you want to execute data prep yourself.\n",
    "if process:\n",
    "\n",
    "    with open(trigram_reviews_filepath, 'w', encoding='utf_8') as f:\n",
    "        \n",
    "        for parsed_review in nlp.pipe(line_review(review_txt_filepath, 0),\n",
    "                                      batch_size=10000, n_threads=4):\n",
    "            \n",
    "            \n",
    "            # lemmatize the text, removing punctuation and whitespace\n",
    "            unigram_review = [token.lemma_ for token in parsed_review\n",
    "                              if not punct_space(token)]\n",
    "            \n",
    "            # apply the first-order and second-order phrase models\n",
    "            bigram_review = bigram_model[unigram_review]\n",
    "            trigram_review = trigram_model[bigram_review]\n",
    "            \n",
    "            # remove any remaining stopwords\n",
    "            trigram_review = [term for term in trigram_review\n",
    "                              if term not in spacy.lang.en.STOP_WORDS]\n",
    "            \n",
    "            # write the transformed review as a line in the new file\n",
    "            trigram_review = u' '.join(trigram_review)\n",
    "            f.write(trigram_review + '\\n')\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original:\n",
      "\n",
      "This place is awesome! Definitely authentic!!!\n",
      "\n",
      "My two favourite dishes are the rice flour rolls and the chicken pho. The rice flour rolls are always fresh whenever I'm there! And the chicken pho is always flavourful!! mmmm....just thinking of it makes me want some!\n",
      "\n",
      "My boyfriend is Vietnamese and he agrees that this place is authentic and one of the best Vietnamese restaurants he has ever eaten at.\n",
      "\n",
      "Prices are very reasonable too!\n",
      "\n",
      "----\n",
      "\n",
      "Transformed:\n",
      "\n",
      "this_place awesome definitely authentic -PRON- favourite dish rice flour roll chicken pho rice flour roll fresh -PRON- chicken pho flavourful mmmm think -PRON- -PRON- want -PRON- boyfriend vietnamese -PRON- agree this_place authentic good vietnamese_restaurant -PRON- eat price very_reasonable\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(u'Original:' + u'\\n')\n",
    "\n",
    "for review in it.islice(line_review(review_txt_filepath), 11, 12):\n",
    "    print(review)\n",
    "\n",
    "print(u'----' + u'\\n')\n",
    "print(u'Transformed:' + u'\\n')\n",
    "\n",
    "with open(trigram_reviews_filepath, encoding='utf_8') as f:\n",
    "    for review in it.islice(f, 11, 12):\n",
    "        print(review)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Topic Modeling with Latent Dirichlet Allocation (_LDA_)\n",
    "\n",
    "*Topic modeling* is family of techniques that can be used to describe and summarize the documents in a corpus according to a set of latent \"topics\". For this demo, we'll be using [*Latent Dirichlet Allocation*](http://www.jmlr.org/papers/volume3/blei03a/blei03a.pdf) or LDA, a popular approach to topic modeling.\n",
    "\n",
    "In many conventional NLP applications, documents are represented a mixture of the individual tokens (words and phrases) they contain. In other words, a document is represented as a *vector* of token counts. There are two layers in this model &mdash; documents and tokens &mdash; and the size or dimensionality of the document vectors is the number of tokens in the corpus vocabulary. This approach has a number of disadvantages:\n",
    "* Document vectors tend to be large (one dimension for each token $\\Rightarrow$ lots of dimensions)\n",
    "* They also tend to be very sparse. Any given document only contains a small fraction of all tokens in the vocabulary, so most values in the document's token vector are 0.\n",
    "* The dimensions are fully indepedent from each other &mdash; there's no sense of connection between related tokens, such as _knife_ and _fork_.\n",
    "\n",
    "LDA injects a third layer into this conceptual model. Documents are represented as a mixture of a pre-defined number of *topics*, and the *topics* are represented as a mixture of the individual tokens in the vocabulary. The number of topics is a model hyperparameter selected by the practitioner. LDA makes a prior assumption that the (document, topic) and (topic, token) mixtures follow [*Dirichlet*](https://en.wikipedia.org/wiki/Dirichlet_distribution) probability distributions. This assumption encourages documents to consist mostly of a handful of topics, and topics to consist mostly of a modest set of the tokens."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from gensim.corpora import Dictionary, MmCorpus\n",
    "from gensim.models.ldamulticore import LdaMulticore\n",
    "\n",
    "import pyLDAvis\n",
    "import pyLDAvis.gensim\n",
    "import warnings\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "trigram_dictionary_filepath = os.path.join(intermediate_directory,\n",
    "                                           'trigram_dict_all.dict')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1.26 s, sys: 19 ms, total: 1.28 s\n",
      "Wall time: 1.34 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# this is a bit time consuming - make the if statement True\n",
    "# if you want to learn the dictionary yourself.\n",
    "if process:\n",
    "\n",
    "    trigram_reviews = LineSentence(trigram_reviews_filepath)\n",
    "\n",
    "    # learn the dictionary by iterating over all of the reviews\n",
    "    trigram_dictionary = Dictionary(trigram_reviews)\n",
    "    \n",
    "    # filter tokens that are very rare or too common from\n",
    "    # the dictionary (filter_extremes) and reassign integer ids (compactify)\n",
    "    trigram_dictionary.filter_extremes(no_below=10, no_above=0.4)\n",
    "    trigram_dictionary.compactify()\n",
    "\n",
    "    trigram_dictionary.save(trigram_dictionary_filepath)\n",
    "    \n",
    "# load the finished dictionary from disk\n",
    "trigram_dictionary = Dictionary.load(trigram_dictionary_filepath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "trigram_bow_filepath = os.path.join(intermediate_directory,\n",
    "                                    'trigram_bow_corpus_all.mm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "def trigram_bow_generator(filepath):\n",
    "    \"\"\"\n",
    "    generator function to read reviews from a file\n",
    "    and yield a bag-of-words representation\n",
    "    \"\"\"\n",
    "    \n",
    "    for review in LineSentence(filepath):\n",
    "        yield trigram_dictionary.doc2bow(review)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1.9 s, sys: 57 ms, total: 1.96 s\n",
      "Wall time: 2.09 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# this is a bit time consuming - make the if statement True\n",
    "# if you want to build the bag-of-words corpus yourself.\n",
    "if process:\n",
    "\n",
    "    # generate bag-of-words representations for\n",
    "    # all reviews and save them as a matrix\n",
    "    MmCorpus.serialize(trigram_bow_filepath,\n",
    "                       trigram_bow_generator(trigram_reviews_filepath))\n",
    "    \n",
    "# load the finished bag-of-words corpus from disk\n",
    "trigram_bow_corpus = MmCorpus(trigram_bow_filepath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "lda_model_filepath = os.path.join(intermediate_directory, 'lda_model_all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Process ForkPoolWorker-10:\n",
      "Process ForkPoolWorker-11:\n",
      "Process ForkPoolWorker-9:\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "  File \"/usr/local/Cellar/python3/3.6.1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/multiprocessing/process.py\", line 249, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/usr/local/Cellar/python3/3.6.1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/multiprocessing/process.py\", line 249, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/usr/local/Cellar/python3/3.6.1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/multiprocessing/process.py\", line 249, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/usr/local/Cellar/python3/3.6.1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/usr/local/Cellar/python3/3.6.1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/usr/local/Cellar/python3/3.6.1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/multiprocessing/pool.py\", line 103, in worker\n",
      "    initializer(*initargs)\n",
      "  File \"/usr/local/Cellar/python3/3.6.1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/usr/local/Cellar/python3/3.6.1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/multiprocessing/pool.py\", line 103, in worker\n",
      "    initializer(*initargs)\n",
      "  File \"/Users/aliosha/Envs/nlp/lib/python3.6/site-packages/gensim/models/ldamulticore.py\", line 286, in worker_e_step\n",
      "    chunk_no, chunk, worker_lda = input_queue.get()\n",
      "  File \"/Users/aliosha/Envs/nlp/lib/python3.6/site-packages/gensim/models/ldamulticore.py\", line 286, in worker_e_step\n",
      "    chunk_no, chunk, worker_lda = input_queue.get()\n",
      "  File \"/usr/local/Cellar/python3/3.6.1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/multiprocessing/pool.py\", line 103, in worker\n",
      "    initializer(*initargs)\n",
      "  File \"/usr/local/Cellar/python3/3.6.1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/multiprocessing/queues.py\", line 94, in get\n",
      "    res = self._recv_bytes()\n",
      "  File \"/usr/local/Cellar/python3/3.6.1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/multiprocessing/connection.py\", line 216, in recv_bytes\n",
      "    buf = self._recv_bytes(maxlength)\n",
      "  File \"/usr/local/Cellar/python3/3.6.1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/multiprocessing/queues.py\", line 93, in get\n",
      "    with self._rlock:\n",
      "  File \"/Users/aliosha/Envs/nlp/lib/python3.6/site-packages/gensim/models/ldamulticore.py\", line 286, in worker_e_step\n",
      "    chunk_no, chunk, worker_lda = input_queue.get()\n",
      "  File \"/usr/local/Cellar/python3/3.6.1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/multiprocessing/queues.py\", line 93, in get\n",
      "    with self._rlock:\n",
      "  File \"/usr/local/Cellar/python3/3.6.1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/multiprocessing/connection.py\", line 407, in _recv_bytes\n",
      "    buf = self._recv(4)\n",
      "  File \"/usr/local/Cellar/python3/3.6.1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/multiprocessing/synchronize.py\", line 96, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/usr/local/Cellar/python3/3.6.1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/multiprocessing/synchronize.py\", line 96, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/usr/local/Cellar/python3/3.6.1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/multiprocessing/connection.py\", line 379, in _recv\n",
      "    chunk = read(handle, remaining)\n",
      "KeyboardInterrupt\n",
      "KeyboardInterrupt\n",
      "KeyboardInterrupt\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<timed exec>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m~/Envs/nlp/lib/python3.6/site-packages/gensim/models/ldamulticore.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, corpus, num_topics, id2word, workers, chunksize, passes, batch, alpha, eta, decay, offset, eval_every, iterations, gamma_threshold, random_state, minimum_probability, minimum_phi_value, per_word_topics, dtype)\u001b[0m\n\u001b[1;32m    151\u001b[0m             \u001b[0mdecay\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdecay\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moffset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moffset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0meval_every\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0meval_every\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0miterations\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0miterations\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m             \u001b[0mgamma_threshold\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mgamma_threshold\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrandom_state\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mminimum_probability\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mminimum_probability\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 153\u001b[0;31m             \u001b[0mminimum_phi_value\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mminimum_phi_value\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mper_word_topics\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mper_word_topics\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    154\u001b[0m         )\n\u001b[1;32m    155\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Envs/nlp/lib/python3.6/site-packages/gensim/models/ldamodel.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, corpus, num_topics, id2word, distributed, chunksize, passes, update_every, alpha, eta, decay, offset, eval_every, iterations, gamma_threshold, minimum_probability, random_state, ns_conf, minimum_phi_value, per_word_topics, callbacks, dtype)\u001b[0m\n\u001b[1;32m    396\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcorpus\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    397\u001b[0m             \u001b[0muse_numpy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdispatcher\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 398\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcorpus\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mchunks_as_numpy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0muse_numpy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    399\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    400\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0minit_dir_prior\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprior\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Envs/nlp/lib/python3.6/site-packages/gensim/models/ldamulticore.py\u001b[0m in \u001b[0;36mupdate\u001b[0;34m(self, corpus, chunks_as_numpy)\u001b[0m\n\u001b[1;32m    266\u001b[0m             \u001b[0;31m# wait for all outstanding jobs to finish\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    267\u001b[0m             \u001b[0;32mwhile\u001b[0m \u001b[0mqueue_size\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 268\u001b[0;31m                 \u001b[0mprocess_result_queue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mforce\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    269\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    270\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mreallen\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mlencorpus\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Envs/nlp/lib/python3.6/site-packages/gensim/models/ldamulticore.py\u001b[0m in \u001b[0;36mprocess_result_queue\u001b[0;34m(force)\u001b[0m\n\u001b[1;32m    228\u001b[0m                 \"\"\"\n\u001b[1;32m    229\u001b[0m                 \u001b[0mmerged_new\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 230\u001b[0;31m                 \u001b[0;32mwhile\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mresult_queue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mempty\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    231\u001b[0m                     \u001b[0mother\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmerge\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult_queue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    232\u001b[0m                     \u001b[0mqueue_size\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m-=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/Cellar/python3/3.6.1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/multiprocessing/queues.py\u001b[0m in \u001b[0;36mempty\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    118\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    119\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mempty\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 120\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_poll\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    121\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    122\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mfull\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/Cellar/python3/3.6.1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/multiprocessing/connection.py\u001b[0m in \u001b[0;36mpoll\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    255\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_closed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    256\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_readable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 257\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_poll\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    258\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    259\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__enter__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/Cellar/python3/3.6.1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/multiprocessing/connection.py\u001b[0m in \u001b[0;36m_poll\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    412\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    413\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_poll\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 414\u001b[0;31m         \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    415\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mbool\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    416\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/Cellar/python3/3.6.1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/multiprocessing/connection.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(object_list, timeout)\u001b[0m\n\u001b[1;32m    903\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0m_WaitSelector\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mselector\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    904\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mobj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mobject_list\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 905\u001b[0;31m                 \u001b[0mselector\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mregister\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mselectors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mEVENT_READ\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    906\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    907\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/Cellar/python3/3.6.1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/selectors.py\u001b[0m in \u001b[0;36mregister\u001b[0;34m(self, fileobj, events, data)\u001b[0m\n\u001b[1;32m    349\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    350\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mregister\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfileobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevents\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 351\u001b[0;31m             \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mregister\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfileobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevents\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    352\u001b[0m             \u001b[0mpoll_events\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    353\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mevents\u001b[0m \u001b[0;34m&\u001b[0m \u001b[0mEVENT_READ\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/Cellar/python3/3.6.1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/selectors.py\u001b[0m in \u001b[0;36mregister\u001b[0;34m(self, fileobj, events, data)\u001b[0m\n\u001b[1;32m    235\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Invalid events: {!r}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevents\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    236\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 237\u001b[0;31m         \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mSelectorKey\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfileobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fileobj_lookup\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfileobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevents\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    238\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    239\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfd\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fd_to_key\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<string>\u001b[0m in \u001b[0;36m__new__\u001b[0;34m(_cls, fileobj, fd, events, data)\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# this is a bit time consuming - make the if statement True\n",
    "# if you want to train the LDA model yourself.\n",
    "\n",
    "topic_num = 10\n",
    "\n",
    "if process:\n",
    "\n",
    "    with warnings.catch_warnings():\n",
    "        warnings.simplefilter('ignore')\n",
    "        \n",
    "        # workers => sets the parallelism, and should be\n",
    "        # set to your number of physical cores minus one\n",
    "        lda = LdaMulticore(trigram_bow_corpus,\n",
    "                           num_topics=topic_num,\n",
    "                           id2word=trigram_dictionary,\n",
    "                           workers=3)\n",
    "    \n",
    "    lda.save(lda_model_filepath)\n",
    "    \n",
    "# load the finished LDA model from disk\n",
    "lda = LdaMulticore.load(lda_model_filepath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 82)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lda.get_topics().shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "def explore_topic(topic_number, topn=25):\n",
    "    \"\"\"\n",
    "    accept a user-supplied topic number and\n",
    "    print out a formatted list of the top terms\n",
    "    \"\"\"\n",
    "        \n",
    "    print(u'{:20} {}'.format(u'term', u'frequency') + u'\\n')\n",
    "\n",
    "    for term, frequency in lda.show_topic(topic_number, topn=25):\n",
    "        print(u'{:20} {:.3f}'.format(term, round(frequency, 3)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "term                 frequency\n",
      "\n",
      "sandwich             0.099\n",
      "time                 0.055\n",
      "restaurant           0.051\n",
      "tasty                0.050\n",
      "place                0.047\n",
      "this_place           0.047\n",
      "chicken              0.047\n",
      "area                 0.047\n",
      "amazing              0.047\n",
      "serve                0.047\n",
      "'s                   0.047\n",
      "soup                 0.033\n",
      "overall              0.014\n",
      "taste                0.013\n",
      "great                0.012\n",
      "tell                 0.012\n",
      "friend               0.011\n",
      "salad                0.010\n",
      "sort_of              0.008\n",
      "ask                  0.008\n",
      "dish                 0.008\n",
      "bread                0.008\n",
      "sure                 0.008\n",
      "atmosphere           0.008\n",
      "review               0.008\n"
     ]
    }
   ],
   "source": [
    "explore_topic(topic_number=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "topic  0\n",
      "term                 frequency\n",
      "\n",
      "this_place           0.075\n",
      "place                0.068\n",
      "ask                  0.036\n",
      "nice                 0.034\n",
      "want                 0.033\n",
      "think                0.028\n",
      "great                0.027\n",
      "salad                0.026\n",
      "do_not               0.026\n",
      "husband              0.024\n",
      "price                0.023\n",
      "sandwich             0.023\n",
      "soup                 0.023\n",
      "service              0.022\n",
      "'s                   0.021\n",
      "try                  0.018\n",
      "thing                0.018\n",
      "eat                  0.017\n",
      "decide               0.016\n",
      "like                 0.016\n",
      "drink                0.016\n",
      "bad                  0.016\n",
      "dish                 0.015\n",
      "atmosphere           0.014\n",
      "chicken              0.014\n",
      "None\n",
      "###\n",
      "\n",
      "topic  1\n",
      "term                 frequency\n",
      "\n",
      "great                0.042\n",
      "come                 0.035\n",
      "service              0.030\n",
      "place                0.029\n",
      "look                 0.028\n",
      "think                0.028\n",
      "delicious            0.026\n",
      "small                0.024\n",
      "drink                0.023\n",
      "like                 0.023\n",
      "try                  0.022\n",
      "'s                   0.021\n",
      "do_not               0.021\n",
      "people               0.020\n",
      "serve                0.020\n",
      "restaurant           0.019\n",
      "seat                 0.019\n",
      "soup                 0.019\n",
      "menu                 0.018\n",
      "dessert              0.018\n",
      "noodle               0.017\n",
      "find                 0.016\n",
      "table                0.016\n",
      "fry                  0.016\n",
      "time                 0.015\n",
      "None\n",
      "###\n",
      "\n",
      "topic  2\n",
      "term                 frequency\n",
      "\n",
      "sandwich             0.099\n",
      "time                 0.055\n",
      "restaurant           0.051\n",
      "tasty                0.050\n",
      "place                0.047\n",
      "this_place           0.047\n",
      "chicken              0.047\n",
      "area                 0.047\n",
      "amazing              0.047\n",
      "serve                0.047\n",
      "'s                   0.047\n",
      "soup                 0.033\n",
      "overall              0.014\n",
      "taste                0.013\n",
      "great                0.012\n",
      "tell                 0.012\n",
      "friend               0.011\n",
      "salad                0.010\n",
      "sort_of              0.008\n",
      "ask                  0.008\n",
      "dish                 0.008\n",
      "bread                0.008\n",
      "sure                 0.008\n",
      "atmosphere           0.008\n",
      "review               0.008\n",
      "None\n",
      "###\n",
      "\n",
      "topic  3\n",
      "term                 frequency\n",
      "\n",
      "serve                0.043\n",
      "great                0.041\n",
      "this_place           0.033\n",
      "service              0.033\n",
      "do_not               0.031\n",
      "time                 0.030\n",
      "price                0.029\n",
      "drink                0.025\n",
      "find                 0.024\n",
      "try                  0.023\n",
      "lot_of               0.022\n",
      "wait                 0.022\n",
      "husband              0.021\n",
      "plate                0.019\n",
      "menu                 0.019\n",
      "dish                 0.019\n",
      "salad                0.019\n",
      "friend               0.018\n",
      "come                 0.017\n",
      "think                0.017\n",
      "like                 0.016\n",
      "come_here            0.016\n",
      "restaurant           0.015\n",
      "choice               0.015\n",
      "nice                 0.015\n",
      "None\n",
      "###\n",
      "\n",
      "topic  4\n",
      "term                 frequency\n",
      "\n",
      "dish                 0.044\n",
      "try                  0.044\n",
      "like                 0.042\n",
      "this_place           0.040\n",
      "do_not               0.039\n",
      "small                0.037\n",
      "meat                 0.035\n",
      "restaurant           0.026\n",
      "place                0.026\n",
      "taste                0.026\n",
      "bit                  0.023\n",
      "eat                  0.023\n",
      "table                0.021\n",
      "think                0.020\n",
      "seat                 0.018\n",
      "thing                0.018\n",
      "serve                0.018\n",
      "service              0.018\n",
      "noodle               0.017\n",
      "delicious            0.016\n",
      "come_out             0.016\n",
      "nice                 0.015\n",
      "find                 0.015\n",
      "plate                0.015\n",
      "great                0.015\n",
      "None\n",
      "###\n",
      "\n",
      "topic  5\n",
      "term                 frequency\n",
      "\n",
      "place                0.056\n",
      "taste                0.047\n",
      "soup                 0.045\n",
      "sure                 0.043\n",
      "nice                 0.043\n",
      "ok                   0.038\n",
      "bland                0.037\n",
      "like                 0.036\n",
      "try                  0.036\n",
      "do_not               0.030\n",
      "salad                0.027\n",
      "think                0.026\n",
      "table                0.026\n",
      "definitely           0.023\n",
      "tasty                0.021\n",
      "wait                 0.021\n",
      "need                 0.021\n",
      "eat                  0.021\n",
      "flavour              0.020\n",
      "come                 0.019\n",
      "huge                 0.019\n",
      "meat                 0.019\n",
      "probably             0.019\n",
      "bread                0.016\n",
      "find                 0.014\n",
      "None\n",
      "###\n",
      "\n",
      "topic  6\n",
      "term                 frequency\n",
      "\n",
      "place                0.041\n",
      "friend               0.037\n",
      "this_place           0.035\n",
      "try                  0.032\n",
      "want                 0.031\n",
      "like                 0.030\n",
      "time                 0.029\n",
      "people               0.029\n",
      "seat                 0.028\n",
      "soup                 0.027\n",
      "ok                   0.023\n",
      "taste                0.023\n",
      "come                 0.022\n",
      "service              0.022\n",
      "great                0.020\n",
      "come_out             0.018\n",
      "work                 0.017\n",
      "come_here            0.017\n",
      "think                0.017\n",
      "bad                  0.017\n",
      "wait                 0.016\n",
      "fry                  0.016\n",
      "price                0.016\n",
      "finally              0.015\n",
      "restaurant           0.015\n",
      "None\n",
      "###\n",
      "\n",
      "topic  7\n",
      "term                 frequency\n",
      "\n",
      "menu                 0.068\n",
      "restaurant           0.054\n",
      "bad                  0.040\n",
      "do_not               0.031\n",
      "this_place           0.030\n",
      "dish                 0.029\n",
      "table                0.024\n",
      "time                 0.024\n",
      "nice                 0.023\n",
      "try                  0.022\n",
      "come                 0.021\n",
      "dinner               0.020\n",
      "lot_of               0.020\n",
      "definitely           0.020\n",
      "decide               0.020\n",
      "friend               0.018\n",
      "overall              0.018\n",
      "pretty               0.017\n",
      "look                 0.017\n",
      "salad                0.016\n",
      "husband              0.016\n",
      "'s                   0.015\n",
      "know                 0.014\n",
      "think                0.014\n",
      "place                0.014\n",
      "None\n",
      "###\n",
      "\n",
      "topic  8\n",
      "term                 frequency\n",
      "\n",
      "try                  0.061\n",
      "definitely           0.049\n",
      "noodle               0.047\n",
      "like                 0.039\n",
      "sandwich             0.039\n",
      "chicken              0.037\n",
      "come                 0.030\n",
      "menu                 0.021\n",
      "decide               0.020\n",
      "wait                 0.019\n",
      "fry                  0.019\n",
      "people               0.019\n",
      "taste                0.018\n",
      "bread                0.018\n",
      "nice                 0.018\n",
      "this_place           0.017\n",
      "eat                  0.017\n",
      "worth                0.016\n",
      "dessert              0.015\n",
      "think                0.015\n",
      "need                 0.015\n",
      "do_not               0.015\n",
      "service              0.014\n",
      "appetizer            0.014\n",
      "'s                   0.013\n",
      "None\n",
      "###\n",
      "\n",
      "topic  9\n",
      "term                 frequency\n",
      "\n",
      "definitely           0.118\n",
      "look                 0.061\n",
      "nice                 0.060\n",
      "small                0.043\n",
      "wait                 0.042\n",
      "share                0.042\n",
      "worth                0.042\n",
      "bad                  0.041\n",
      "try                  0.040\n",
      "different            0.040\n",
      "place                0.039\n",
      "appetizer            0.025\n",
      "want                 0.023\n",
      "service              0.022\n",
      "on_top               0.022\n",
      "menu                 0.021\n",
      "ask                  0.021\n",
      "probably             0.021\n",
      "bit                  0.021\n",
      "delicious            0.021\n",
      "amazing              0.021\n",
      "tasty                0.021\n",
      "flavour              0.020\n",
      "eat                  0.020\n",
      "dessert              0.005\n",
      "None\n",
      "###\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for topic in range(topic_num):\n",
    "    print(\"topic \", topic)\n",
    "    print(explore_topic(topic_number=topic, topn=10))\n",
    "    print('###')\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "LDAvis_data_filepath = os.path.join(intermediate_directory, 'ldavis_prepared')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2.24 ms, sys: 1.04 ms, total: 3.29 ms\n",
      "Wall time: 20.8 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# this is a bit time consuming - make the if statement True\n",
    "# if you want to execute data prep yourself.\n",
    "if process:\n",
    "\n",
    "    LDAvis_prepared = pyLDAvis.gensim.prepare(lda, trigram_bow_corpus,\n",
    "                                              trigram_dictionary, mds='mmds')\n",
    "\n",
    "    with open(LDAvis_data_filepath, 'wb') as f:\n",
    "        pickle.dump(LDAvis_prepared, f)\n",
    "        \n",
    "# load the pre-prepared pyLDAvis data from disk\n",
    "with open(LDAvis_data_filepath, 'rb') as f:\n",
    "    LDAvis_prepared = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load the pre-prepared pyLDAvis data from disk\n",
    "with open(LDAvis_data_filepath, 'rb') as f:\n",
    "    LDAvis_prepared = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.css\">\n",
       "\n",
       "\n",
       "<div id=\"ldavis_el9156746366703761782912016\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "\n",
       "var ldavis_el9156746366703761782912016_data = {\"mdsDat\": {\"Freq\": [18.929506301879883, 16.351158142089844, 15.231828689575195, 13.80599308013916, 12.43419361114502, 10.921781539916992, 5.413443088531494, 3.187260866165161, 2.6629300117492676, 1.061905860900879], \"cluster\": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1], \"topics\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], \"x\": [0.02776939321555229, 0.0026907365643567185, -0.059309771547819516, -0.010616195673327418, 0.0432175527593132, 0.09523790802131603, 4.403271591458433e-05, -0.1253097684427188, -0.10499592001140357, 0.1312720323988165], \"y\": [0.03790395734804027, -0.04141730863588475, -0.03720557790847037, 0.019060369717916675, -0.03756243041343285, -0.0169605115763003, 0.09723640415190196, -0.16432663499537112, 0.05204938807058099, 0.09122234424101955]}, \"tinfo\": {\"Category\": [\"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", 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\"choice\", \"choice\", \"come\", \"come\", \"come\", \"come\", \"come\", \"come\", \"come\", \"come\", \"come_here\", \"come_here\", \"come_here\", \"come_here\", \"come_here\", \"come_here\", \"come_out\", \"come_out\", \"come_out\", \"come_out\", \"come_out\", \"come_out\", \"decide\", \"decide\", \"decide\", \"decide\", \"decide\", \"decide\", \"definitely\", \"definitely\", \"definitely\", \"definitely\", \"definitely\", \"definitely\", \"definitely\", \"definitely\", \"definitely\", \"delicious\", \"delicious\", \"delicious\", \"delicious\", \"delicious\", \"delicious\", \"delicious\", \"dessert\", \"dessert\", \"dessert\", \"dessert\", \"dessert\", \"dessert\", \"different\", \"different\", \"different\", \"different\", \"different\", \"different\", \"dinner\", \"dinner\", \"dinner\", \"dinner\", \"dinner\", \"dinner\", \"dish\", \"dish\", \"dish\", \"dish\", \"dish\", \"dish\", \"dish\", \"do_not\", \"do_not\", \"do_not\", \"do_not\", \"do_not\", \"do_not\", \"do_not\", \"do_not\", \"drink\", \"drink\", \"drink\", \"drink\", \"drink\", \"drink\", \"drink\", \"eat\", \"eat\", \"eat\", \"eat\", \"eat\", \"eat\", \"eat\", \"eat\", \"finally\", \"finally\", \"finally\", \"finally\", \"finally\", \"finally\", \"find\", \"find\", \"find\", \"find\", \"find\", \"find\", \"find\", \"flavour\", \"flavour\", \"flavour\", \"flavour\", \"flavour\", \"flavour\", \"flavour\", \"flavour\", \"friend\", \"friend\", \"friend\", \"friend\", \"friend\", \"friend\", \"friend\", \"fry\", \"fry\", \"fry\", \"fry\", \"fry\", \"fry\", \"great\", \"great\", \"great\", \"great\", \"great\", \"great\", \"great\", \"great\", \"huge\", \"huge\", \"huge\", \"huge\", \"huge\", \"huge\", \"huge\", \"husband\", \"husband\", \"husband\", \"husband\", \"husband\", \"husband\", \"husband\", \"know\", \"know\", \"know\", \"know\", \"know\", \"know\", \"like\", \"like\", \"like\", \"like\", \"like\", \"like\", \"like\", \"like\", \"little\", \"little\", \"little\", \"little\", \"little\", \"little\", \"little\", \"look\", \"look\", \"look\", \"look\", \"look\", \"look\", \"look\", \"lot_of\", \"lot_of\", \"lot_of\", \"lot_of\", \"make_sure\", \"make_sure\", \"make_sure\", \"make_sure\", \"meat\", \"meat\", \"meat\", \"meat\", \"meat\", \"meat\", \"meat\", \"menu\", \"menu\", \"menu\", \"menu\", \"menu\", \"menu\", \"menu\", \"need\", \"need\", \"need\", \"need\", \"need\", \"need\", \"need\", \"nice\", \"nice\", \"nice\", \"nice\", \"nice\", \"nice\", \"nice\", \"nice\", \"nice\", \"noodle\", \"noodle\", \"noodle\", \"noodle\", \"noodle\", \"nothing_special\", \"nothing_special\", \"nothing_special\", \"nothing_special\", \"nothing_special\", \"nothing_special\", \"nothing_special\", \"ok\", \"ok\", \"ok\", \"ok\", \"ok\", \"ok\", \"on_top\", \"on_top\", \"on_top\", \"on_top\", \"on_top\", \"on_top\", \"on_top\", \"outside\", \"outside\", \"outside\", \"outside\", \"outside\", \"outside\", \"overall\", \"overall\", \"overall\", \"overall\", \"overall\", \"overall\", \"people\", \"people\", \"people\", \"people\", \"people\", \"place\", \"place\", \"place\", \"place\", \"place\", \"place\", \"place\", \"place\", \"place\", \"place\", \"plate\", \"plate\", \"plate\", \"plate\", \"plate\", \"plate\", \"pretty\", \"pretty\", \"pretty\", \"pretty\", \"pretty\", \"pretty\", \"pretty\", \"price\", \"price\", \"price\", \"price\", \"price\", \"price\", \"probably\", \"probably\", \"probably\", \"probably\", \"probably\", \"probably\", \"probably\", \"restaurant\", \"restaurant\", \"restaurant\", \"restaurant\", \"restaurant\", \"restaurant\", \"restaurant\", \"restaurant\", \"review\", \"review\", \"review\", \"review\", \"review\", \"review\", \"salad\", \"salad\", \"salad\", \"salad\", \"salad\", \"salad\", \"salad\", \"salad\", \"sandwich\", \"sandwich\", \"sandwich\", \"sandwich\", \"sandwich\", \"sandwich\", \"sauce\", \"sauce\", \"sauce\", \"sauce\", \"sauce\", \"sauce\", \"sauce\", \"seat\", \"seat\", \"seat\", \"seat\", \"seat\", \"seat\", \"serve\", \"serve\", \"serve\", \"serve\", \"serve\", \"serve\", \"serve\", \"service\", \"service\", \"service\", \"service\", \"service\", \"service\", \"service\", \"service\", \"share\", \"share\", \"share\", \"share\", \"share\", \"share\", \"share\", \"small\", \"small\", \"small\", \"small\", \"small\", \"small\", \"small\", \"sort_of\", \"sort_of\", \"sort_of\", \"sort_of\", \"sort_of\", \"sort_of\", \"sort_of\", \"soup\", \"soup\", \"soup\", \"soup\", \"soup\", \"soup\", \"soup\", \"soup\", \"space\", \"space\", \"space\", \"space\", \"space\", \"sure\", \"sure\", \"sure\", \"sure\", \"sure\", \"sure\", \"sure\", \"table\", \"table\", \"table\", \"table\", \"table\", \"table\", \"table\", \"table\", \"taste\", \"taste\", \"taste\", \"taste\", \"taste\", \"taste\", \"taste\", \"tasty\", \"tasty\", \"tasty\", \"tasty\", \"tasty\", \"tasty\", \"tasty\", \"tasty\", \"tell\", \"tell\", \"tell\", \"tell\", \"tell\", \"tell\", \"tell\", \"thing\", \"thing\", \"thing\", \"thing\", \"thing\", \"thing\", \"thing\", \"think\", \"think\", \"think\", \"think\", \"think\", \"think\", \"think\", \"think\", \"this_place\", \"this_place\", \"this_place\", \"this_place\", \"this_place\", \"this_place\", \"this_place\", \"this_place\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"try\", \"try\", \"try\", \"try\", \"try\", \"try\", \"try\", \"try\", \"try\", \"wait\", \"wait\", \"wait\", \"wait\", \"wait\", \"wait\", \"wait\", \"wait\", \"wait\", \"want\", \"want\", \"want\", \"want\", \"want\", \"want\", \"want\", \"want\", \"work\", \"work\", \"work\", \"work\", \"work\", \"work\", \"work\", \"worth\", \"worth\", \"worth\", \"worth\", \"worth\", \"worth\"]}, \"R\": 30, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [4, 5, 9, 2, 7, 1, 8, 10, 6, 3]};\n",
       "\n",
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       "}\n",
       "\n",
       "if(typeof(LDAvis) !== \"undefined\"){\n",
       "   // already loaded: just create the visualization\n",
       "   !function(LDAvis){\n",
       "       new LDAvis(\"#\" + \"ldavis_el9156746366703761782912016\", ldavis_el9156746366703761782912016_data);\n",
       "   }(LDAvis);\n",
       "}else if(typeof define === \"function\" && define.amd){\n",
       "   // require.js is available: use it to load d3/LDAvis\n",
       "   require.config({paths: {d3: \"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min\"}});\n",
       "   require([\"d3\"], function(d3){\n",
       "      window.d3 = d3;\n",
       "      LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
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       "      });\n",
       "    });\n",
       "}else{\n",
       "    // require.js not available: dynamically load d3 & LDAvis\n",
       "    LDAvis_load_lib(\"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min.js\", function(){\n",
       "         LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
       "                 new LDAvis(\"#\" + \"ldavis_el9156746366703761782912016\", ldavis_el9156746366703761782912016_data);\n",
       "            })\n",
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       "}\n",
       "</script>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pyLDAvis.display(LDAvis_prepared)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_sample_review(review_number):\n",
    "    \"\"\"\n",
    "    retrieve a particular review index\n",
    "    from the reviews file and return it\n",
    "    \"\"\"\n",
    "    \n",
    "    return list(it.islice(line_review(review_txt_filepath),\n",
    "                          review_number, review_number+1))[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "def lda_description(review_text, min_topic_freq=0.05):\n",
    "    \"\"\"\n",
    "    accept the original text of a review and (1) parse it with spaCy,\n",
    "    (2) apply text pre-proccessing steps, (3) create a bag-of-words\n",
    "    representation, (4) create an LDA representation, and\n",
    "    (5) print a sorted list of the top topics in the LDA representation\n",
    "    \"\"\"\n",
    "    \n",
    "    # parse the review text with spaCy\n",
    "    parsed_review = nlp(review_text)\n",
    "    \n",
    "    # lemmatize the text and remove punctuation and whitespace\n",
    "    unigram_review = [token.lemma_ for token in parsed_review\n",
    "                      if not punct_space(token)]\n",
    "    \n",
    "    # apply the first-order and secord-order phrase models\n",
    "    bigram_review = bigram_model[unigram_review]\n",
    "    trigram_review = trigram_model[bigram_review]\n",
    "    \n",
    "    # remove any remaining stopwords\n",
    "    trigram_review = [term for term in trigram_review\n",
    "                      if not term in spacy.lang.en.STOP_WORDS]\n",
    "    \n",
    "    # create a bag-of-words representation\n",
    "    review_bow = trigram_dictionary.doc2bow(trigram_review)\n",
    "    \n",
    "    # create an LDA representation\n",
    "    review_lda = lda[review_bow]\n",
    "    \n",
    "    # sort with the most highly related topics first\n",
    "    review_lda = sorted(review_lda, key=lambda X : -X[1])\n",
    "    \n",
    "    for topic_number, freq in review_lda:\n",
    "        if freq < min_topic_freq:\n",
    "            break\n",
    "            \n",
    "        # print the most highly related topic names and frequencies\n",
    "        print('{:25} {}'.format(topic_number,\n",
    "                                round(freq, 3)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Small little Japanese restaurant in the Don Mills neighbourhood. Lots of different rolls to pick from. \n",
      "\n",
      "Great lunch special selection.\n",
      "\n",
      "Service is a little slow though. So if you only have 1 hour for lunch, may not be the best place. Take out is pretty quick though.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "sample_review = get_sample_review(50)\n",
    "print(sample_review)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/aliosha/Envs/nlp/lib/python3.6/site-packages/gensim/models/phrases.py:486: UserWarning: For a faster implementation, use the gensim.models.phrases.Phraser class\n",
      "  warnings.warn(\"For a faster implementation, use the gensim.models.phrases.Phraser class\")\n"
     ]
    },
    {
     "ename": "IndexError",
     "evalue": "index 138 is out of bounds for axis 1 with size 82",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-100-5738a3945149>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlda_description\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msample_review\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-98-5dc7db1ed794>\u001b[0m in \u001b[0;36mlda_description\u001b[0;34m(review_text, min_topic_freq)\u001b[0m\n\u001b[1;32m     26\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     27\u001b[0m     \u001b[0;31m# create an LDA representation\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 28\u001b[0;31m     \u001b[0mreview_lda\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlda\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mreview_bow\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     29\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     30\u001b[0m     \u001b[0;31m# sort with the most highly related topics first\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Envs/nlp/lib/python3.6/site-packages/gensim/models/ldamodel.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, bow, eps)\u001b[0m\n\u001b[1;32m   1185\u001b[0m             \u001b[0;31m`\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtopic_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtopic_probability\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;31m`\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mtuples\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1186\u001b[0m         \"\"\"\n\u001b[0;32m-> 1187\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_document_topics\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbow\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0meps\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mminimum_phi_value\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mper_word_topics\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1188\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1189\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mignore\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'state'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'dispatcher'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mseparately\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Envs/nlp/lib/python3.6/site-packages/gensim/models/ldamodel.py\u001b[0m in \u001b[0;36mget_document_topics\u001b[0;34m(self, bow, minimum_probability, minimum_phi_value, per_word_topics)\u001b[0m\n\u001b[1;32m   1022\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcorpus\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1023\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1024\u001b[0;31m         \u001b[0mgamma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mphis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minference\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mbow\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcollect_sstats\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mper_word_topics\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1025\u001b[0m         \u001b[0mtopic_dist\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgamma\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgamma\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# normalize distribution\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1026\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Envs/nlp/lib/python3.6/site-packages/gensim/models/ldamodel.py\u001b[0m in \u001b[0;36minference\u001b[0;34m(self, chunk, collect_sstats)\u001b[0m\n\u001b[1;32m    506\u001b[0m             \u001b[0mElogthetad\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mElogtheta\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    507\u001b[0m             \u001b[0mexpElogthetad\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mexpElogtheta\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 508\u001b[0;31m             \u001b[0mexpElogbetad\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexpElogbeta\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mids\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    509\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    510\u001b[0m             \u001b[0;31m# The optimal phi_{dwk} is proportional to expElogthetad_k * expElogbetad_w.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mIndexError\u001b[0m: index 138 is out of bounds for axis 1 with size 82"
     ]
    }
   ],
   "source": [
    "lda_description(sample_review)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This place is a bit of an adventure...at least for me. I've never had charcuterie before and I'm not generally a big meat eater, so this was a little out of the box. That said, I like trying to be adventurous and trying new things, so when are visiting friends suggested we give it a go, we went for it. Dinner service is open at 6pm, so we agreed we'd arrive at 6pm. We got there at about 6:15....there was already a HUGE wait for a table, but we managed to wrangle seats at the bar. \n",
      "\n",
      "The four of us ordered a bunch of dishes to split between ourselves. We tried the charcuterie (sort of a platter of assorted sliced meats and pâté), horse tar tar, roasted bone marrow, pork carnitas tacos, tongue on brioche and the foie gras and nutella for dessert. \n",
      "\n",
      "The dishes came out staggered, so we got to sit around chatting and enjoying ourselves and our cocktails. The charcuterie came out first. It was assorted sliced meats arranged on a wood plank and served with bread. It was actually really nice. Next was the horse tar tar. This was a stretch for me. I don't generally like to eat animals that I LIKE. The people I was with convinced me to try a small bit and I must say, it tasted pretty good, but on principle, I just have trouble with it. Sorry! I hate being one of those people who doesn't have a problem killing the \"ugly\" animals, but doesn't want the cute ones to die...but I am. \n",
      "\n",
      "Next was the bone marrow, which was....well let's just say I had to keep telling myself not to think about what I was eating. Basically, it's a big bone and the marrow is of course in the middle and has been cooked so that it's almost the consistency of warm butter...pretty fatty and oily looking. You scoop it out with a spoon and put it on bread. It's actually delicious, but feels like you're eating something you shouldn't, purely for health reasons if nothing else! It feels like eating pure fat....tastes as good I suppose. \n",
      "\n",
      "We had the tongue on brioche, which I was only okay with. The tongue was sliced and presented as a sort of Montreal smoked meat. We were told to open it up and eat it like an open faced sandwich. This made it easier to split certainly. The meat itself was very tender and the brioche was great, but the meat had a horseradish (I think) sauce, which was a bit overpowering. \n",
      "\n",
      "Finally we had the tacos, which was probably the more \"normal\" thing we had. I actually thought those were my favorite. Wish it was one of the more exciting things, but nope. Those tacos were excellent. \n",
      "\n",
      "The foie gras with nutella was also delicious. It was served on a banana bread, over a nutella spread...but the fois gras was superflous. It didn't add any taste and was overpowered but the nutella and basically didn't add anything to the dish. Could have been served without. Clearly just added to SAY it was there. \n",
      "\n",
      "So what's the overall opinion? Well, the restaurant is too small. It's REALLY small, but popular. Wish it was in a bigger location. Sitting at the bar was kind of interesting, because all the cooking was done behind the bar, but that was a small workspace and it got hot, because the oven, dishwasher, stove top and so forth were always turned up very hight. Impressive how they work in such a space though.\n",
      "\n",
      "Overall the food was very good. SOMEONE knows what they are doing. However, it is quite a pricey place and as I said, sometimes the flavours overpower the meat, or the meat just isn't even necessary. Sometimes you're paying extra for fancy meat dishes that don't even need the meat, or where you can't TASTE the meat. \n",
      "\n",
      "Those are the only criticisms, but I wouldn't let that keep you from trying this place...but only if you're willing to go out on a limb a bit and as something different. This isn't a go-to place, but it's a fun place to have gone to! \n",
      "\n",
      "Oh...PS - They don't accept credit cards. Only debit or cash.\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/aliosha/Envs/nlp/lib/python3.6/site-packages/gensim/models/phrases.py:486: UserWarning: For a faster implementation, use the gensim.models.phrases.Phraser class\n",
      "  warnings.warn(\"For a faster implementation, use the gensim.models.phrases.Phraser class\")\n"
     ]
    },
    {
     "ename": "IndexError",
     "evalue": "index 82 is out of bounds for axis 1 with size 82",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-101-62f2d68063ec>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0msample_review2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_sample_review\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msample_review2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mlda_description\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msample_review2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-98-5dc7db1ed794>\u001b[0m in \u001b[0;36mlda_description\u001b[0;34m(review_text, min_topic_freq)\u001b[0m\n\u001b[1;32m     26\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     27\u001b[0m     \u001b[0;31m# create an LDA representation\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 28\u001b[0;31m     \u001b[0mreview_lda\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlda\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mreview_bow\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     29\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     30\u001b[0m     \u001b[0;31m# sort with the most highly related topics first\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Envs/nlp/lib/python3.6/site-packages/gensim/models/ldamodel.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, bow, eps)\u001b[0m\n\u001b[1;32m   1185\u001b[0m             \u001b[0;31m`\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtopic_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtopic_probability\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;31m`\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mtuples\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1186\u001b[0m         \"\"\"\n\u001b[0;32m-> 1187\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_document_topics\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbow\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0meps\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mminimum_phi_value\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mper_word_topics\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1188\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1189\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mignore\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'state'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'dispatcher'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mseparately\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Envs/nlp/lib/python3.6/site-packages/gensim/models/ldamodel.py\u001b[0m in \u001b[0;36mget_document_topics\u001b[0;34m(self, bow, minimum_probability, minimum_phi_value, per_word_topics)\u001b[0m\n\u001b[1;32m   1022\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcorpus\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1023\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1024\u001b[0;31m         \u001b[0mgamma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mphis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minference\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mbow\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcollect_sstats\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mper_word_topics\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1025\u001b[0m         \u001b[0mtopic_dist\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgamma\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgamma\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# normalize distribution\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1026\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Envs/nlp/lib/python3.6/site-packages/gensim/models/ldamodel.py\u001b[0m in \u001b[0;36minference\u001b[0;34m(self, chunk, collect_sstats)\u001b[0m\n\u001b[1;32m    506\u001b[0m             \u001b[0mElogthetad\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mElogtheta\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    507\u001b[0m             \u001b[0mexpElogthetad\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mexpElogtheta\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 508\u001b[0;31m             \u001b[0mexpElogbetad\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexpElogbeta\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mids\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    509\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    510\u001b[0m             \u001b[0;31m# The optimal phi_{dwk} is proportional to expElogthetad_k * expElogbetad_w.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mIndexError\u001b[0m: index 82 is out of bounds for axis 1 with size 82"
     ]
    }
   ],
   "source": [
    "sample_review2 = get_sample_review(100)\n",
    "print(sample_review2)\n",
    "lda_description(sample_review2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "from gensim.models import Word2Vec\n",
    "\n",
    "trigram_sentences = LineSentence(trigram_sentences_filepath)\n",
    "word2vec_filepath = os.path.join(intermediate_directory, 'word2vec_model_all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/aliosha/Envs/nlp/lib/python3.6/site-packages/gensim/utils.py:568: DeprecationWarning: Call to deprecated `cum_table` (Attribute will be removed in 4.0.0, use self.vocabulary.cum_table instead).\n",
      "  if hasattr(self, attrib):\n",
      "/Users/aliosha/Envs/nlp/lib/python3.6/site-packages/gensim/utils.py:569: DeprecationWarning: Call to deprecated `cum_table` (Attribute will be removed in 4.0.0, use self.vocabulary.cum_table instead).\n",
      "  asides[attrib] = getattr(self, attrib)\n",
      "/Users/aliosha/Envs/nlp/lib/python3.6/site-packages/gensim/utils.py:570: DeprecationWarning: Call to deprecated `cum_table` (Attribute will be removed in 4.0.0, use self.vocabulary.cum_table instead).\n",
      "  delattr(self, attrib)\n",
      "/Users/aliosha/Envs/nlp/lib/python3.6/site-packages/gensim/utils.py:525: DeprecationWarning: Call to deprecated `cum_table` (Attribute will be removed in 4.0.0, use self.vocabulary.cum_table instead).\n",
      "  setattr(obj, attrib, val)\n",
      "/Users/aliosha/Envs/nlp/lib/python3.6/site-packages/gensim/utils.py:568: DeprecationWarning: Call to deprecated `cum_table` (Attribute will be removed in 4.0.0, use self.vocabulary.cum_table instead).\n",
      "  if hasattr(self, attrib):\n",
      "/Users/aliosha/Envs/nlp/lib/python3.6/site-packages/gensim/utils.py:569: DeprecationWarning: Call to deprecated `cum_table` (Attribute will be removed in 4.0.0, use self.vocabulary.cum_table instead).\n",
      "  asides[attrib] = getattr(self, attrib)\n",
      "/Users/aliosha/Envs/nlp/lib/python3.6/site-packages/gensim/utils.py:570: DeprecationWarning: Call to deprecated `cum_table` (Attribute will be removed in 4.0.0, use self.vocabulary.cum_table instead).\n",
      "  delattr(self, attrib)\n",
      "/Users/aliosha/Envs/nlp/lib/python3.6/site-packages/gensim/utils.py:525: DeprecationWarning: Call to deprecated `cum_table` (Attribute will be removed in 4.0.0, use self.vocabulary.cum_table instead).\n",
      "  setattr(obj, attrib, val)\n",
      "/Users/aliosha/Envs/nlp/lib/python3.6/site-packages/gensim/utils.py:568: DeprecationWarning: Call to deprecated `cum_table` (Attribute will be removed in 4.0.0, use self.vocabulary.cum_table instead).\n",
      "  if hasattr(self, attrib):\n",
      "/Users/aliosha/Envs/nlp/lib/python3.6/site-packages/gensim/utils.py:569: DeprecationWarning: Call to deprecated `cum_table` (Attribute will be removed in 4.0.0, use self.vocabulary.cum_table instead).\n",
      "  asides[attrib] = getattr(self, attrib)\n",
      "/Users/aliosha/Envs/nlp/lib/python3.6/site-packages/gensim/utils.py:570: DeprecationWarning: Call to deprecated `cum_table` (Attribute will be removed in 4.0.0, use self.vocabulary.cum_table instead).\n",
      "  delattr(self, attrib)\n",
      "/Users/aliosha/Envs/nlp/lib/python3.6/site-packages/gensim/utils.py:525: DeprecationWarning: Call to deprecated `cum_table` (Attribute will be removed in 4.0.0, use self.vocabulary.cum_table instead).\n",
      "  setattr(obj, attrib, val)\n",
      "/Users/aliosha/Envs/nlp/lib/python3.6/site-packages/gensim/utils.py:568: DeprecationWarning: Call to deprecated `cum_table` (Attribute will be removed in 4.0.0, use self.vocabulary.cum_table instead).\n",
      "  if hasattr(self, attrib):\n",
      "/Users/aliosha/Envs/nlp/lib/python3.6/site-packages/gensim/utils.py:569: DeprecationWarning: Call to deprecated `cum_table` (Attribute will be removed in 4.0.0, use self.vocabulary.cum_table instead).\n",
      "  asides[attrib] = getattr(self, attrib)\n",
      "/Users/aliosha/Envs/nlp/lib/python3.6/site-packages/gensim/utils.py:570: DeprecationWarning: Call to deprecated `cum_table` (Attribute will be removed in 4.0.0, use self.vocabulary.cum_table instead).\n",
      "  delattr(self, attrib)\n",
      "/Users/aliosha/Envs/nlp/lib/python3.6/site-packages/gensim/utils.py:525: DeprecationWarning: Call to deprecated `cum_table` (Attribute will be removed in 4.0.0, use self.vocabulary.cum_table instead).\n",
      "  setattr(obj, attrib, val)\n",
      "/Users/aliosha/Envs/nlp/lib/python3.6/site-packages/gensim/utils.py:568: DeprecationWarning: Call to deprecated `cum_table` (Attribute will be removed in 4.0.0, use self.vocabulary.cum_table instead).\n",
      "  if hasattr(self, attrib):\n",
      "/Users/aliosha/Envs/nlp/lib/python3.6/site-packages/gensim/utils.py:569: DeprecationWarning: Call to deprecated `cum_table` (Attribute will be removed in 4.0.0, use self.vocabulary.cum_table instead).\n",
      "  asides[attrib] = getattr(self, attrib)\n",
      "/Users/aliosha/Envs/nlp/lib/python3.6/site-packages/gensim/utils.py:570: DeprecationWarning: Call to deprecated `cum_table` (Attribute will be removed in 4.0.0, use self.vocabulary.cum_table instead).\n",
      "  delattr(self, attrib)\n",
      "/Users/aliosha/Envs/nlp/lib/python3.6/site-packages/gensim/utils.py:525: DeprecationWarning: Call to deprecated `cum_table` (Attribute will be removed in 4.0.0, use self.vocabulary.cum_table instead).\n",
      "  setattr(obj, attrib, val)\n",
      "/Users/aliosha/Envs/nlp/lib/python3.6/site-packages/gensim/utils.py:568: DeprecationWarning: Call to deprecated `cum_table` (Attribute will be removed in 4.0.0, use self.vocabulary.cum_table instead).\n",
      "  if hasattr(self, attrib):\n",
      "/Users/aliosha/Envs/nlp/lib/python3.6/site-packages/gensim/utils.py:569: DeprecationWarning: Call to deprecated `cum_table` (Attribute will be removed in 4.0.0, use self.vocabulary.cum_table instead).\n",
      "  asides[attrib] = getattr(self, attrib)\n",
      "/Users/aliosha/Envs/nlp/lib/python3.6/site-packages/gensim/utils.py:570: DeprecationWarning: Call to deprecated `cum_table` (Attribute will be removed in 4.0.0, use self.vocabulary.cum_table instead).\n",
      "  delattr(self, attrib)\n",
      "/Users/aliosha/Envs/nlp/lib/python3.6/site-packages/gensim/utils.py:525: DeprecationWarning: Call to deprecated `cum_table` (Attribute will be removed in 4.0.0, use self.vocabulary.cum_table instead).\n",
      "  setattr(obj, attrib, val)\n",
      "/Users/aliosha/Envs/nlp/lib/python3.6/site-packages/gensim/utils.py:568: DeprecationWarning: Call to deprecated `cum_table` (Attribute will be removed in 4.0.0, use self.vocabulary.cum_table instead).\n",
      "  if hasattr(self, attrib):\n",
      "/Users/aliosha/Envs/nlp/lib/python3.6/site-packages/gensim/utils.py:569: DeprecationWarning: Call to deprecated `cum_table` (Attribute will be removed in 4.0.0, use self.vocabulary.cum_table instead).\n",
      "  asides[attrib] = getattr(self, attrib)\n",
      "/Users/aliosha/Envs/nlp/lib/python3.6/site-packages/gensim/utils.py:570: DeprecationWarning: Call to deprecated `cum_table` (Attribute will be removed in 4.0.0, use self.vocabulary.cum_table instead).\n",
      "  delattr(self, attrib)\n",
      "/Users/aliosha/Envs/nlp/lib/python3.6/site-packages/gensim/utils.py:525: DeprecationWarning: Call to deprecated `cum_table` (Attribute will be removed in 4.0.0, use self.vocabulary.cum_table instead).\n",
      "  setattr(obj, attrib, val)\n",
      "/Users/aliosha/Envs/nlp/lib/python3.6/site-packages/gensim/utils.py:568: DeprecationWarning: Call to deprecated `cum_table` (Attribute will be removed in 4.0.0, use self.vocabulary.cum_table instead).\n",
      "  if hasattr(self, attrib):\n",
      "/Users/aliosha/Envs/nlp/lib/python3.6/site-packages/gensim/utils.py:569: DeprecationWarning: Call to deprecated `cum_table` (Attribute will be removed in 4.0.0, use self.vocabulary.cum_table instead).\n",
      "  asides[attrib] = getattr(self, attrib)\n",
      "/Users/aliosha/Envs/nlp/lib/python3.6/site-packages/gensim/utils.py:570: DeprecationWarning: Call to deprecated `cum_table` (Attribute will be removed in 4.0.0, use self.vocabulary.cum_table instead).\n",
      "  delattr(self, attrib)\n",
      "/Users/aliosha/Envs/nlp/lib/python3.6/site-packages/gensim/utils.py:525: DeprecationWarning: Call to deprecated `cum_table` (Attribute will be removed in 4.0.0, use self.vocabulary.cum_table instead).\n",
      "  setattr(obj, attrib, val)\n",
      "/Users/aliosha/Envs/nlp/lib/python3.6/site-packages/gensim/utils.py:568: DeprecationWarning: Call to deprecated `cum_table` (Attribute will be removed in 4.0.0, use self.vocabulary.cum_table instead).\n",
      "  if hasattr(self, attrib):\n",
      "/Users/aliosha/Envs/nlp/lib/python3.6/site-packages/gensim/utils.py:569: DeprecationWarning: Call to deprecated `cum_table` (Attribute will be removed in 4.0.0, use self.vocabulary.cum_table instead).\n",
      "  asides[attrib] = getattr(self, attrib)\n",
      "/Users/aliosha/Envs/nlp/lib/python3.6/site-packages/gensim/utils.py:570: DeprecationWarning: Call to deprecated `cum_table` (Attribute will be removed in 4.0.0, use self.vocabulary.cum_table instead).\n",
      "  delattr(self, attrib)\n",
      "/Users/aliosha/Envs/nlp/lib/python3.6/site-packages/gensim/utils.py:525: DeprecationWarning: Call to deprecated `cum_table` (Attribute will be removed in 4.0.0, use self.vocabulary.cum_table instead).\n",
      "  setattr(obj, attrib, val)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/aliosha/Envs/nlp/lib/python3.6/site-packages/gensim/utils.py:568: DeprecationWarning: Call to deprecated `cum_table` (Attribute will be removed in 4.0.0, use self.vocabulary.cum_table instead).\n",
      "  if hasattr(self, attrib):\n",
      "/Users/aliosha/Envs/nlp/lib/python3.6/site-packages/gensim/utils.py:569: DeprecationWarning: Call to deprecated `cum_table` (Attribute will be removed in 4.0.0, use self.vocabulary.cum_table instead).\n",
      "  asides[attrib] = getattr(self, attrib)\n",
      "/Users/aliosha/Envs/nlp/lib/python3.6/site-packages/gensim/utils.py:570: DeprecationWarning: Call to deprecated `cum_table` (Attribute will be removed in 4.0.0, use self.vocabulary.cum_table instead).\n",
      "  delattr(self, attrib)\n",
      "/Users/aliosha/Envs/nlp/lib/python3.6/site-packages/gensim/utils.py:525: DeprecationWarning: Call to deprecated `cum_table` (Attribute will be removed in 4.0.0, use self.vocabulary.cum_table instead).\n",
      "  setattr(obj, attrib, val)\n",
      "/Users/aliosha/Envs/nlp/lib/python3.6/site-packages/gensim/utils.py:568: DeprecationWarning: Call to deprecated `cum_table` (Attribute will be removed in 4.0.0, use self.vocabulary.cum_table instead).\n",
      "  if hasattr(self, attrib):\n",
      "/Users/aliosha/Envs/nlp/lib/python3.6/site-packages/gensim/utils.py:569: DeprecationWarning: Call to deprecated `cum_table` (Attribute will be removed in 4.0.0, use self.vocabulary.cum_table instead).\n",
      "  asides[attrib] = getattr(self, attrib)\n",
      "/Users/aliosha/Envs/nlp/lib/python3.6/site-packages/gensim/utils.py:570: DeprecationWarning: Call to deprecated `cum_table` (Attribute will be removed in 4.0.0, use self.vocabulary.cum_table instead).\n",
      "  delattr(self, attrib)\n",
      "/Users/aliosha/Envs/nlp/lib/python3.6/site-packages/gensim/utils.py:525: DeprecationWarning: Call to deprecated `cum_table` (Attribute will be removed in 4.0.0, use self.vocabulary.cum_table instead).\n",
      "  setattr(obj, attrib, val)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12 training epochs so far.\n",
      "CPU times: user 6min 20s, sys: 4.2 s, total: 6min 25s\n",
      "Wall time: 2min 34s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/aliosha/Envs/nlp/lib/python3.6/site-packages/gensim/utils.py:568: DeprecationWarning: Call to deprecated `cum_table` (Attribute will be removed in 4.0.0, use self.vocabulary.cum_table instead).\n",
      "  if hasattr(self, attrib):\n",
      "/Users/aliosha/Envs/nlp/lib/python3.6/site-packages/gensim/utils.py:569: DeprecationWarning: Call to deprecated `cum_table` (Attribute will be removed in 4.0.0, use self.vocabulary.cum_table instead).\n",
      "  asides[attrib] = getattr(self, attrib)\n",
      "/Users/aliosha/Envs/nlp/lib/python3.6/site-packages/gensim/utils.py:570: DeprecationWarning: Call to deprecated `cum_table` (Attribute will be removed in 4.0.0, use self.vocabulary.cum_table instead).\n",
      "  delattr(self, attrib)\n",
      "/Users/aliosha/Envs/nlp/lib/python3.6/site-packages/gensim/utils.py:525: DeprecationWarning: Call to deprecated `cum_table` (Attribute will be removed in 4.0.0, use self.vocabulary.cum_table instead).\n",
      "  setattr(obj, attrib, val)\n",
      "/Users/aliosha/Envs/nlp/lib/python3.6/site-packages/gensim/utils.py:461: DeprecationWarning: Call to deprecated `cum_table` (Attribute will be removed in 4.0.0, use self.vocabulary.cum_table instead).\n",
      "  setattr(self, attrib, None)\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# this is a bit time consuming - make the if statement True\n",
    "# if you want to train the word2vec model yourself.\n",
    "if process:\n",
    "\n",
    "    # initiate the model and perform the first epoch of training\n",
    "    food2vec = Word2Vec(trigram_sentences, size=100, window=5,\n",
    "                        min_count=20, sg=1, workers=4)\n",
    "    \n",
    "    food2vec.save(word2vec_filepath)\n",
    "\n",
    "    # perform another 11 epochs of training\n",
    "    for i in range(1,12):\n",
    "        food2vec.train(trigram_sentences, total_examples=food2vec.corpus_count, epochs=food2vec.epochs)\n",
    "        food2vec.save(word2vec_filepath)\n",
    "        \n",
    "# load the finished model from disk\n",
    "food2vec = Word2Vec.load(word2vec_filepath)\n",
    "food2vec.init_sims()\n",
    "\n",
    "print(u'{} training epochs so far.'.format(food2vec.train_count))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3,629 terms in the food2vec vocabulary.\n"
     ]
    }
   ],
   "source": [
    "print(u'{:,} terms in the food2vec vocabulary.'.format(len(food2vec.wv.vocab)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>90</th>\n",
       "      <th>91</th>\n",
       "      <th>92</th>\n",
       "      <th>93</th>\n",
       "      <th>94</th>\n",
       "      <th>95</th>\n",
       "      <th>96</th>\n",
       "      <th>97</th>\n",
       "      <th>98</th>\n",
       "      <th>99</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>-PRON-</th>\n",
       "      <td>-0.192540</td>\n",
       "      <td>-0.137421</td>\n",
       "      <td>0.087687</td>\n",
       "      <td>-0.041500</td>\n",
       "      <td>-0.046415</td>\n",
       "      <td>0.062379</td>\n",
       "      <td>0.016884</td>\n",
       "      <td>-0.103175</td>\n",
       "      <td>0.140363</td>\n",
       "      <td>0.088608</td>\n",
       "      <td>...</td>\n",
       "      <td>0.015603</td>\n",
       "      <td>-0.126255</td>\n",
       "      <td>-0.051399</td>\n",
       "      <td>0.037474</td>\n",
       "      <td>0.139312</td>\n",
       "      <td>0.042000</td>\n",
       "      <td>-0.059149</td>\n",
       "      <td>0.078348</td>\n",
       "      <td>-0.050734</td>\n",
       "      <td>-0.068732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>be</th>\n",
       "      <td>-0.096318</td>\n",
       "      <td>-0.144332</td>\n",
       "      <td>-0.212839</td>\n",
       "      <td>-0.052480</td>\n",
       "      <td>0.036218</td>\n",
       "      <td>0.219431</td>\n",
       "      <td>0.000228</td>\n",
       "      <td>-0.034399</td>\n",
       "      <td>0.202455</td>\n",
       "      <td>-0.034905</td>\n",
       "      <td>...</td>\n",
       "      <td>0.006305</td>\n",
       "      <td>0.000102</td>\n",
       "      <td>0.030234</td>\n",
       "      <td>0.043527</td>\n",
       "      <td>-0.017617</td>\n",
       "      <td>0.009483</td>\n",
       "      <td>-0.145317</td>\n",
       "      <td>0.228525</td>\n",
       "      <td>-0.069701</td>\n",
       "      <td>-0.114176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>the</th>\n",
       "      <td>0.000089</td>\n",
       "      <td>-0.028026</td>\n",
       "      <td>-0.041021</td>\n",
       "      <td>0.111821</td>\n",
       "      <td>-0.085819</td>\n",
       "      <td>0.043335</td>\n",
       "      <td>-0.056748</td>\n",
       "      <td>-0.131266</td>\n",
       "      <td>0.042512</td>\n",
       "      <td>0.022696</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.188833</td>\n",
       "      <td>-0.068218</td>\n",
       "      <td>-0.112790</td>\n",
       "      <td>0.042619</td>\n",
       "      <td>-0.024181</td>\n",
       "      <td>0.014776</td>\n",
       "      <td>-0.080565</td>\n",
       "      <td>0.028031</td>\n",
       "      <td>-0.114328</td>\n",
       "      <td>0.064669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>and</th>\n",
       "      <td>-0.189819</td>\n",
       "      <td>-0.073866</td>\n",
       "      <td>0.065609</td>\n",
       "      <td>0.040589</td>\n",
       "      <td>-0.076059</td>\n",
       "      <td>0.059223</td>\n",
       "      <td>0.021162</td>\n",
       "      <td>-0.162560</td>\n",
       "      <td>0.112582</td>\n",
       "      <td>-0.030728</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.106540</td>\n",
       "      <td>0.071831</td>\n",
       "      <td>-0.049181</td>\n",
       "      <td>0.066906</td>\n",
       "      <td>-0.054017</td>\n",
       "      <td>-0.077172</td>\n",
       "      <td>-0.196286</td>\n",
       "      <td>0.115795</td>\n",
       "      <td>-0.091203</td>\n",
       "      <td>0.083549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>0.030527</td>\n",
       "      <td>0.019898</td>\n",
       "      <td>-0.173009</td>\n",
       "      <td>0.110138</td>\n",
       "      <td>0.061045</td>\n",
       "      <td>0.147410</td>\n",
       "      <td>0.076303</td>\n",
       "      <td>-0.185216</td>\n",
       "      <td>-0.074776</td>\n",
       "      <td>-0.000041</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.015731</td>\n",
       "      <td>0.120600</td>\n",
       "      <td>-0.101515</td>\n",
       "      <td>0.006418</td>\n",
       "      <td>-0.044200</td>\n",
       "      <td>-0.026002</td>\n",
       "      <td>-0.085152</td>\n",
       "      <td>0.075556</td>\n",
       "      <td>0.042961</td>\n",
       "      <td>-0.011100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>to</th>\n",
       "      <td>-0.184892</td>\n",
       "      <td>0.053593</td>\n",
       "      <td>0.148398</td>\n",
       "      <td>-0.002422</td>\n",
       "      <td>-0.278245</td>\n",
       "      <td>0.127144</td>\n",
       "      <td>-0.046509</td>\n",
       "      <td>-0.088389</td>\n",
       "      <td>0.231082</td>\n",
       "      <td>0.091001</td>\n",
       "      <td>...</td>\n",
       "      <td>0.131239</td>\n",
       "      <td>0.017966</td>\n",
       "      <td>-0.044981</td>\n",
       "      <td>0.002161</td>\n",
       "      <td>0.141389</td>\n",
       "      <td>-0.029075</td>\n",
       "      <td>-0.197160</td>\n",
       "      <td>-0.052363</td>\n",
       "      <td>-0.029980</td>\n",
       "      <td>-0.062437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>have</th>\n",
       "      <td>-0.263948</td>\n",
       "      <td>-0.139392</td>\n",
       "      <td>-0.094716</td>\n",
       "      <td>-0.105031</td>\n",
       "      <td>0.029591</td>\n",
       "      <td>0.186383</td>\n",
       "      <td>0.089885</td>\n",
       "      <td>-0.073477</td>\n",
       "      <td>0.127601</td>\n",
       "      <td>-0.038056</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.042434</td>\n",
       "      <td>0.003974</td>\n",
       "      <td>-0.080431</td>\n",
       "      <td>-0.017601</td>\n",
       "      <td>0.019746</td>\n",
       "      <td>0.111616</td>\n",
       "      <td>-0.128615</td>\n",
       "      <td>0.125177</td>\n",
       "      <td>-0.054404</td>\n",
       "      <td>0.176189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>of</th>\n",
       "      <td>-0.064054</td>\n",
       "      <td>-0.021256</td>\n",
       "      <td>-0.037077</td>\n",
       "      <td>-0.136387</td>\n",
       "      <td>0.130824</td>\n",
       "      <td>0.011794</td>\n",
       "      <td>-0.070115</td>\n",
       "      <td>-0.086504</td>\n",
       "      <td>0.034637</td>\n",
       "      <td>0.240778</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.142515</td>\n",
       "      <td>0.128248</td>\n",
       "      <td>-0.085780</td>\n",
       "      <td>0.114970</td>\n",
       "      <td>-0.082709</td>\n",
       "      <td>0.076574</td>\n",
       "      <td>-0.083384</td>\n",
       "      <td>0.117512</td>\n",
       "      <td>-0.182946</td>\n",
       "      <td>-0.000966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for</th>\n",
       "      <td>-0.010958</td>\n",
       "      <td>-0.093656</td>\n",
       "      <td>-0.013771</td>\n",
       "      <td>0.135409</td>\n",
       "      <td>0.151720</td>\n",
       "      <td>0.223458</td>\n",
       "      <td>0.132373</td>\n",
       "      <td>0.050078</td>\n",
       "      <td>0.031994</td>\n",
       "      <td>0.018673</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.091269</td>\n",
       "      <td>0.116898</td>\n",
       "      <td>0.030665</td>\n",
       "      <td>-0.040892</td>\n",
       "      <td>0.132972</td>\n",
       "      <td>-0.132993</td>\n",
       "      <td>-0.290694</td>\n",
       "      <td>0.164789</td>\n",
       "      <td>-0.119832</td>\n",
       "      <td>-0.133160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>in</th>\n",
       "      <td>-0.127789</td>\n",
       "      <td>-0.014818</td>\n",
       "      <td>-0.066206</td>\n",
       "      <td>-0.095683</td>\n",
       "      <td>0.045466</td>\n",
       "      <td>0.096631</td>\n",
       "      <td>0.072912</td>\n",
       "      <td>0.010753</td>\n",
       "      <td>0.061598</td>\n",
       "      <td>0.327738</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.108266</td>\n",
       "      <td>0.009744</td>\n",
       "      <td>-0.000948</td>\n",
       "      <td>0.109708</td>\n",
       "      <td>0.077226</td>\n",
       "      <td>0.040942</td>\n",
       "      <td>-0.131442</td>\n",
       "      <td>-0.055605</td>\n",
       "      <td>-0.031663</td>\n",
       "      <td>-0.078908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>but</th>\n",
       "      <td>-0.066095</td>\n",
       "      <td>-0.054027</td>\n",
       "      <td>-0.013163</td>\n",
       "      <td>-0.014823</td>\n",
       "      <td>0.035345</td>\n",
       "      <td>0.067532</td>\n",
       "      <td>0.114754</td>\n",
       "      <td>-0.133493</td>\n",
       "      <td>0.132913</td>\n",
       "      <td>-0.042661</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.021344</td>\n",
       "      <td>-0.036936</td>\n",
       "      <td>-0.045068</td>\n",
       "      <td>0.127355</td>\n",
       "      <td>0.098319</td>\n",
       "      <td>-0.041496</td>\n",
       "      <td>-0.162874</td>\n",
       "      <td>0.141484</td>\n",
       "      <td>-0.122386</td>\n",
       "      <td>-0.212305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>not</th>\n",
       "      <td>-0.238327</td>\n",
       "      <td>-0.041270</td>\n",
       "      <td>-0.091936</td>\n",
       "      <td>-0.080716</td>\n",
       "      <td>0.024456</td>\n",
       "      <td>0.114123</td>\n",
       "      <td>0.166103</td>\n",
       "      <td>-0.062798</td>\n",
       "      <td>0.103472</td>\n",
       "      <td>-0.115738</td>\n",
       "      <td>...</td>\n",
       "      <td>0.057384</td>\n",
       "      <td>-0.056605</td>\n",
       "      <td>0.091968</td>\n",
       "      <td>0.078609</td>\n",
       "      <td>0.035846</td>\n",
       "      <td>-0.090300</td>\n",
       "      <td>0.067451</td>\n",
       "      <td>0.088992</td>\n",
       "      <td>-0.082238</td>\n",
       "      <td>-0.216122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>with</th>\n",
       "      <td>-0.151605</td>\n",
       "      <td>-0.094535</td>\n",
       "      <td>0.031022</td>\n",
       "      <td>0.052123</td>\n",
       "      <td>-0.020034</td>\n",
       "      <td>0.242343</td>\n",
       "      <td>0.020790</td>\n",
       "      <td>-0.083308</td>\n",
       "      <td>-0.038762</td>\n",
       "      <td>0.030932</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.114223</td>\n",
       "      <td>0.168517</td>\n",
       "      <td>0.001207</td>\n",
       "      <td>0.102143</td>\n",
       "      <td>0.013646</td>\n",
       "      <td>-0.129282</td>\n",
       "      <td>-0.103153</td>\n",
       "      <td>0.054679</td>\n",
       "      <td>-0.032169</td>\n",
       "      <td>0.095618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>that</th>\n",
       "      <td>-0.102382</td>\n",
       "      <td>0.051184</td>\n",
       "      <td>0.072436</td>\n",
       "      <td>0.008235</td>\n",
       "      <td>-0.062911</td>\n",
       "      <td>0.022180</td>\n",
       "      <td>0.005541</td>\n",
       "      <td>-0.065420</td>\n",
       "      <td>0.128774</td>\n",
       "      <td>-0.036637</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.083903</td>\n",
       "      <td>-0.016001</td>\n",
       "      <td>-0.075827</td>\n",
       "      <td>0.135004</td>\n",
       "      <td>0.061166</td>\n",
       "      <td>0.068840</td>\n",
       "      <td>-0.101208</td>\n",
       "      <td>-0.042464</td>\n",
       "      <td>-0.104981</td>\n",
       "      <td>-0.136647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>good</th>\n",
       "      <td>-0.188190</td>\n",
       "      <td>-0.067030</td>\n",
       "      <td>-0.128134</td>\n",
       "      <td>-0.042151</td>\n",
       "      <td>0.127970</td>\n",
       "      <td>0.109065</td>\n",
       "      <td>0.065108</td>\n",
       "      <td>-0.068333</td>\n",
       "      <td>0.096048</td>\n",
       "      <td>-0.027358</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.113426</td>\n",
       "      <td>-0.200908</td>\n",
       "      <td>-0.002371</td>\n",
       "      <td>-0.086590</td>\n",
       "      <td>-0.028552</td>\n",
       "      <td>0.098042</td>\n",
       "      <td>0.027771</td>\n",
       "      <td>-0.002126</td>\n",
       "      <td>0.019668</td>\n",
       "      <td>-0.142265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>on</th>\n",
       "      <td>-0.005899</td>\n",
       "      <td>-0.084236</td>\n",
       "      <td>0.072148</td>\n",
       "      <td>-0.039223</td>\n",
       "      <td>-0.083173</td>\n",
       "      <td>0.205746</td>\n",
       "      <td>0.260500</td>\n",
       "      <td>0.025992</td>\n",
       "      <td>-0.087802</td>\n",
       "      <td>0.113882</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.134531</td>\n",
       "      <td>0.032244</td>\n",
       "      <td>-0.011631</td>\n",
       "      <td>0.016936</td>\n",
       "      <td>0.081113</td>\n",
       "      <td>0.130330</td>\n",
       "      <td>-0.197432</td>\n",
       "      <td>0.109674</td>\n",
       "      <td>0.067194</td>\n",
       "      <td>0.069555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>food</th>\n",
       "      <td>-0.094605</td>\n",
       "      <td>-0.057565</td>\n",
       "      <td>0.019090</td>\n",
       "      <td>0.052923</td>\n",
       "      <td>-0.105340</td>\n",
       "      <td>-0.125611</td>\n",
       "      <td>0.026780</td>\n",
       "      <td>0.035995</td>\n",
       "      <td>0.088988</td>\n",
       "      <td>0.218870</td>\n",
       "      <td>...</td>\n",
       "      <td>0.052335</td>\n",
       "      <td>-0.060964</td>\n",
       "      <td>0.073406</td>\n",
       "      <td>-0.170049</td>\n",
       "      <td>0.042704</td>\n",
       "      <td>0.177785</td>\n",
       "      <td>-0.025385</td>\n",
       "      <td>-0.051239</td>\n",
       "      <td>-0.159380</td>\n",
       "      <td>-0.175729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>so</th>\n",
       "      <td>-0.183660</td>\n",
       "      <td>-0.048636</td>\n",
       "      <td>-0.066389</td>\n",
       "      <td>-0.172881</td>\n",
       "      <td>-0.075315</td>\n",
       "      <td>0.127804</td>\n",
       "      <td>0.082498</td>\n",
       "      <td>-0.145854</td>\n",
       "      <td>0.085634</td>\n",
       "      <td>0.055793</td>\n",
       "      <td>...</td>\n",
       "      <td>0.066474</td>\n",
       "      <td>-0.010231</td>\n",
       "      <td>0.013490</td>\n",
       "      <td>0.105681</td>\n",
       "      <td>0.002420</td>\n",
       "      <td>0.025109</td>\n",
       "      <td>-0.201466</td>\n",
       "      <td>-0.063068</td>\n",
       "      <td>-0.132008</td>\n",
       "      <td>-0.203403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>this</th>\n",
       "      <td>-0.108534</td>\n",
       "      <td>0.065841</td>\n",
       "      <td>0.105023</td>\n",
       "      <td>-0.070618</td>\n",
       "      <td>-0.065963</td>\n",
       "      <td>-0.033980</td>\n",
       "      <td>0.057006</td>\n",
       "      <td>0.035344</td>\n",
       "      <td>-0.072082</td>\n",
       "      <td>0.004064</td>\n",
       "      <td>...</td>\n",
       "      <td>0.096331</td>\n",
       "      <td>-0.084680</td>\n",
       "      <td>-0.015909</td>\n",
       "      <td>0.048753</td>\n",
       "      <td>0.116532</td>\n",
       "      <td>0.058992</td>\n",
       "      <td>-0.022128</td>\n",
       "      <td>-0.092712</td>\n",
       "      <td>-0.167243</td>\n",
       "      <td>-0.204690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>get</th>\n",
       "      <td>-0.089895</td>\n",
       "      <td>-0.160190</td>\n",
       "      <td>-0.204633</td>\n",
       "      <td>0.145030</td>\n",
       "      <td>0.118558</td>\n",
       "      <td>0.124394</td>\n",
       "      <td>0.054298</td>\n",
       "      <td>-0.095441</td>\n",
       "      <td>-0.004761</td>\n",
       "      <td>0.035745</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.103003</td>\n",
       "      <td>-0.002285</td>\n",
       "      <td>0.077270</td>\n",
       "      <td>0.122984</td>\n",
       "      <td>0.155547</td>\n",
       "      <td>0.094730</td>\n",
       "      <td>-0.203995</td>\n",
       "      <td>-0.077664</td>\n",
       "      <td>0.046396</td>\n",
       "      <td>0.044063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>order</th>\n",
       "      <td>-0.095767</td>\n",
       "      <td>-0.231510</td>\n",
       "      <td>0.041440</td>\n",
       "      <td>0.074546</td>\n",
       "      <td>-0.036252</td>\n",
       "      <td>0.052206</td>\n",
       "      <td>0.049430</td>\n",
       "      <td>-0.073733</td>\n",
       "      <td>-0.031316</td>\n",
       "      <td>-0.016103</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.051730</td>\n",
       "      <td>0.077677</td>\n",
       "      <td>0.050358</td>\n",
       "      <td>0.009253</td>\n",
       "      <td>-0.002400</td>\n",
       "      <td>0.193037</td>\n",
       "      <td>-0.060212</td>\n",
       "      <td>0.041352</td>\n",
       "      <td>0.068094</td>\n",
       "      <td>0.041941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>at</th>\n",
       "      <td>0.083019</td>\n",
       "      <td>-0.009276</td>\n",
       "      <td>0.154966</td>\n",
       "      <td>-0.084689</td>\n",
       "      <td>0.097699</td>\n",
       "      <td>-0.075665</td>\n",
       "      <td>0.232719</td>\n",
       "      <td>0.066325</td>\n",
       "      <td>0.010844</td>\n",
       "      <td>0.178534</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.035956</td>\n",
       "      <td>0.047810</td>\n",
       "      <td>-0.118697</td>\n",
       "      <td>0.056097</td>\n",
       "      <td>0.154180</td>\n",
       "      <td>0.023770</td>\n",
       "      <td>-0.113843</td>\n",
       "      <td>-0.051588</td>\n",
       "      <td>-0.062524</td>\n",
       "      <td>-0.109468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>go</th>\n",
       "      <td>0.048564</td>\n",
       "      <td>-0.091995</td>\n",
       "      <td>0.026214</td>\n",
       "      <td>0.021247</td>\n",
       "      <td>-0.092897</td>\n",
       "      <td>-0.015113</td>\n",
       "      <td>0.095624</td>\n",
       "      <td>-0.149511</td>\n",
       "      <td>0.115619</td>\n",
       "      <td>0.056114</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.164015</td>\n",
       "      <td>0.002612</td>\n",
       "      <td>0.245725</td>\n",
       "      <td>-0.016533</td>\n",
       "      <td>0.021000</td>\n",
       "      <td>0.242736</td>\n",
       "      <td>-0.064401</td>\n",
       "      <td>-0.078001</td>\n",
       "      <td>0.005967</td>\n",
       "      <td>-0.004110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>there</th>\n",
       "      <td>-0.008382</td>\n",
       "      <td>-0.107287</td>\n",
       "      <td>-0.119996</td>\n",
       "      <td>-0.053642</td>\n",
       "      <td>-0.133700</td>\n",
       "      <td>-0.029199</td>\n",
       "      <td>0.304989</td>\n",
       "      <td>0.083237</td>\n",
       "      <td>-0.030373</td>\n",
       "      <td>0.079556</td>\n",
       "      <td>...</td>\n",
       "      <td>0.024329</td>\n",
       "      <td>0.071463</td>\n",
       "      <td>0.119142</td>\n",
       "      <td>-0.080713</td>\n",
       "      <td>0.001862</td>\n",
       "      <td>-0.009744</td>\n",
       "      <td>-0.183875</td>\n",
       "      <td>-0.009156</td>\n",
       "      <td>-0.153777</td>\n",
       "      <td>-0.019262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>great</th>\n",
       "      <td>-0.117353</td>\n",
       "      <td>-0.018317</td>\n",
       "      <td>-0.026486</td>\n",
       "      <td>0.144878</td>\n",
       "      <td>0.142012</td>\n",
       "      <td>0.094409</td>\n",
       "      <td>0.014710</td>\n",
       "      <td>-0.114920</td>\n",
       "      <td>0.112542</td>\n",
       "      <td>0.031354</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.214965</td>\n",
       "      <td>0.072440</td>\n",
       "      <td>0.065399</td>\n",
       "      <td>-0.138333</td>\n",
       "      <td>-0.238280</td>\n",
       "      <td>0.055685</td>\n",
       "      <td>0.005077</td>\n",
       "      <td>0.012492</td>\n",
       "      <td>-0.005923</td>\n",
       "      <td>-0.070824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>like</th>\n",
       "      <td>-0.146573</td>\n",
       "      <td>-0.125287</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>-0.027444</td>\n",
       "      <td>0.021749</td>\n",
       "      <td>0.216425</td>\n",
       "      <td>0.054879</td>\n",
       "      <td>0.024403</td>\n",
       "      <td>0.066836</td>\n",
       "      <td>0.046442</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.221343</td>\n",
       "      <td>0.021544</td>\n",
       "      <td>-0.080988</td>\n",
       "      <td>0.149703</td>\n",
       "      <td>-0.023309</td>\n",
       "      <td>0.086434</td>\n",
       "      <td>-0.059255</td>\n",
       "      <td>0.038443</td>\n",
       "      <td>-0.084650</td>\n",
       "      <td>-0.064201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>do_not</th>\n",
       "      <td>-0.204803</td>\n",
       "      <td>0.029435</td>\n",
       "      <td>-0.027627</td>\n",
       "      <td>0.043308</td>\n",
       "      <td>-0.058731</td>\n",
       "      <td>-0.054174</td>\n",
       "      <td>0.023838</td>\n",
       "      <td>-0.067478</td>\n",
       "      <td>0.064517</td>\n",
       "      <td>0.018739</td>\n",
       "      <td>...</td>\n",
       "      <td>0.008481</td>\n",
       "      <td>-0.116913</td>\n",
       "      <td>0.088326</td>\n",
       "      <td>0.137177</td>\n",
       "      <td>0.030670</td>\n",
       "      <td>-0.000638</td>\n",
       "      <td>-0.056274</td>\n",
       "      <td>0.001611</td>\n",
       "      <td>-0.168155</td>\n",
       "      <td>-0.020512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>as</th>\n",
       "      <td>-0.141327</td>\n",
       "      <td>-0.184162</td>\n",
       "      <td>-0.011112</td>\n",
       "      <td>-0.077010</td>\n",
       "      <td>-0.049078</td>\n",
       "      <td>0.037249</td>\n",
       "      <td>0.023733</td>\n",
       "      <td>-0.118470</td>\n",
       "      <td>0.044099</td>\n",
       "      <td>0.045384</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.010985</td>\n",
       "      <td>-0.070742</td>\n",
       "      <td>-0.099017</td>\n",
       "      <td>0.078669</td>\n",
       "      <td>-0.066038</td>\n",
       "      <td>0.064837</td>\n",
       "      <td>-0.204999</td>\n",
       "      <td>0.165826</td>\n",
       "      <td>-0.144399</td>\n",
       "      <td>-0.207110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>just</th>\n",
       "      <td>-0.075492</td>\n",
       "      <td>-0.153972</td>\n",
       "      <td>0.198078</td>\n",
       "      <td>0.134122</td>\n",
       "      <td>-0.151163</td>\n",
       "      <td>0.167485</td>\n",
       "      <td>0.094396</td>\n",
       "      <td>-0.093465</td>\n",
       "      <td>0.172112</td>\n",
       "      <td>0.076595</td>\n",
       "      <td>...</td>\n",
       "      <td>0.029857</td>\n",
       "      <td>0.039638</td>\n",
       "      <td>0.003459</td>\n",
       "      <td>0.001979</td>\n",
       "      <td>-0.003595</td>\n",
       "      <td>0.060887</td>\n",
       "      <td>-0.128413</td>\n",
       "      <td>-0.020340</td>\n",
       "      <td>-0.081539</td>\n",
       "      <td>-0.192929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>all</th>\n",
       "      <td>-0.194047</td>\n",
       "      <td>-0.017328</td>\n",
       "      <td>0.187311</td>\n",
       "      <td>-0.005889</td>\n",
       "      <td>0.019718</td>\n",
       "      <td>-0.101647</td>\n",
       "      <td>0.101547</td>\n",
       "      <td>-0.101570</td>\n",
       "      <td>0.018934</td>\n",
       "      <td>0.133588</td>\n",
       "      <td>...</td>\n",
       "      <td>0.068676</td>\n",
       "      <td>0.148213</td>\n",
       "      <td>0.002361</td>\n",
       "      <td>0.141743</td>\n",
       "      <td>-0.164167</td>\n",
       "      <td>0.123084</td>\n",
       "      <td>0.011680</td>\n",
       "      <td>0.053275</td>\n",
       "      <td>-0.155196</td>\n",
       "      <td>-0.088775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>soupe</th>\n",
       "      <td>-0.022476</td>\n",
       "      <td>-0.118028</td>\n",
       "      <td>-0.036387</td>\n",
       "      <td>-0.042614</td>\n",
       "      <td>0.003509</td>\n",
       "      <td>-0.047582</td>\n",
       "      <td>0.136152</td>\n",
       "      <td>-0.118974</td>\n",
       "      <td>-0.090659</td>\n",
       "      <td>-0.025446</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.226941</td>\n",
       "      <td>0.035727</td>\n",
       "      <td>0.093376</td>\n",
       "      <td>-0.036261</td>\n",
       "      <td>-0.101395</td>\n",
       "      <td>-0.056403</td>\n",
       "      <td>-0.146233</td>\n",
       "      <td>-0.097134</td>\n",
       "      <td>0.002730</td>\n",
       "      <td>-0.030946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>caviar</th>\n",
       "      <td>-0.135327</td>\n",
       "      <td>0.034995</td>\n",
       "      <td>-0.024154</td>\n",
       "      <td>-0.076070</td>\n",
       "      <td>0.078668</td>\n",
       "      <td>0.064858</td>\n",
       "      <td>0.001998</td>\n",
       "      <td>-0.001652</td>\n",
       "      <td>-0.105238</td>\n",
       "      <td>0.018555</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.022385</td>\n",
       "      <td>0.087690</td>\n",
       "      <td>-0.013808</td>\n",
       "      <td>-0.042190</td>\n",
       "      <td>-0.189940</td>\n",
       "      <td>0.142276</td>\n",
       "      <td>-0.064569</td>\n",
       "      <td>0.083470</td>\n",
       "      <td>-0.128207</td>\n",
       "      <td>-0.148390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>too_greasy</th>\n",
       "      <td>-0.022927</td>\n",
       "      <td>0.011734</td>\n",
       "      <td>-0.067743</td>\n",
       "      <td>0.087502</td>\n",
       "      <td>-0.034038</td>\n",
       "      <td>0.042902</td>\n",
       "      <td>-0.217221</td>\n",
       "      <td>-0.036918</td>\n",
       "      <td>0.050670</td>\n",
       "      <td>-0.025139</td>\n",
       "      <td>...</td>\n",
       "      <td>0.033056</td>\n",
       "      <td>-0.150457</td>\n",
       "      <td>0.030881</td>\n",
       "      <td>-0.085927</td>\n",
       "      <td>-0.056622</td>\n",
       "      <td>0.019331</td>\n",
       "      <td>0.114762</td>\n",
       "      <td>0.128876</td>\n",
       "      <td>0.078853</td>\n",
       "      <td>0.012402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>turkey_burger</th>\n",
       "      <td>-0.210112</td>\n",
       "      <td>-0.075608</td>\n",
       "      <td>0.123027</td>\n",
       "      <td>-0.144845</td>\n",
       "      <td>-0.065669</td>\n",
       "      <td>0.159550</td>\n",
       "      <td>0.038045</td>\n",
       "      <td>-0.097440</td>\n",
       "      <td>0.047505</td>\n",
       "      <td>-0.112820</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.033055</td>\n",
       "      <td>-0.091538</td>\n",
       "      <td>-0.240900</td>\n",
       "      <td>-0.220910</td>\n",
       "      <td>-0.092483</td>\n",
       "      <td>0.021548</td>\n",
       "      <td>-0.027130</td>\n",
       "      <td>-0.128636</td>\n",
       "      <td>-0.086874</td>\n",
       "      <td>0.102681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>about_8</th>\n",
       "      <td>0.074899</td>\n",
       "      <td>0.066140</td>\n",
       "      <td>-0.087793</td>\n",
       "      <td>-0.110130</td>\n",
       "      <td>0.096991</td>\n",
       "      <td>-0.022319</td>\n",
       "      <td>0.102327</td>\n",
       "      <td>0.001946</td>\n",
       "      <td>-0.017980</td>\n",
       "      <td>0.154447</td>\n",
       "      <td>...</td>\n",
       "      <td>0.060591</td>\n",
       "      <td>0.142302</td>\n",
       "      <td>-0.063091</td>\n",
       "      <td>-0.093958</td>\n",
       "      <td>-0.028988</td>\n",
       "      <td>0.018035</td>\n",
       "      <td>-0.131597</td>\n",
       "      <td>0.201788</td>\n",
       "      <td>-0.019231</td>\n",
       "      <td>-0.012712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>popcorn_chicken</th>\n",
       "      <td>-0.051711</td>\n",
       "      <td>-0.070500</td>\n",
       "      <td>0.237497</td>\n",
       "      <td>0.128638</td>\n",
       "      <td>-0.050679</td>\n",
       "      <td>-0.015431</td>\n",
       "      <td>-0.071123</td>\n",
       "      <td>0.022381</td>\n",
       "      <td>0.091113</td>\n",
       "      <td>0.078115</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.027268</td>\n",
       "      <td>-0.141960</td>\n",
       "      <td>-0.050201</td>\n",
       "      <td>0.016049</td>\n",
       "      <td>-0.222791</td>\n",
       "      <td>-0.193952</td>\n",
       "      <td>-0.109775</td>\n",
       "      <td>0.015500</td>\n",
       "      <td>0.071373</td>\n",
       "      <td>-0.027344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>theatre</th>\n",
       "      <td>0.021344</td>\n",
       "      <td>0.009175</td>\n",
       "      <td>0.140688</td>\n",
       "      <td>-0.049587</td>\n",
       "      <td>-0.126946</td>\n",
       "      <td>0.052697</td>\n",
       "      <td>0.235300</td>\n",
       "      <td>-0.051903</td>\n",
       "      <td>0.099603</td>\n",
       "      <td>0.121937</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.049365</td>\n",
       "      <td>-0.096886</td>\n",
       "      <td>0.088006</td>\n",
       "      <td>0.099423</td>\n",
       "      <td>-0.134382</td>\n",
       "      <td>0.042007</td>\n",
       "      <td>0.002219</td>\n",
       "      <td>-0.004592</td>\n",
       "      <td>0.161482</td>\n",
       "      <td>-0.063428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>star_rating</th>\n",
       "      <td>-0.073516</td>\n",
       "      <td>-0.214689</td>\n",
       "      <td>0.126476</td>\n",
       "      <td>-0.117121</td>\n",
       "      <td>0.050298</td>\n",
       "      <td>0.124286</td>\n",
       "      <td>-0.033591</td>\n",
       "      <td>-0.089336</td>\n",
       "      <td>0.108219</td>\n",
       "      <td>-0.004200</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.054798</td>\n",
       "      <td>-0.169646</td>\n",
       "      <td>-0.097593</td>\n",
       "      <td>0.003510</td>\n",
       "      <td>-0.103876</td>\n",
       "      <td>0.105514</td>\n",
       "      <td>-0.118692</td>\n",
       "      <td>0.039148</td>\n",
       "      <td>-0.041415</td>\n",
       "      <td>-0.075414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>about_$_10</th>\n",
       "      <td>0.081247</td>\n",
       "      <td>-0.105166</td>\n",
       "      <td>0.159298</td>\n",
       "      <td>0.339183</td>\n",
       "      <td>0.169080</td>\n",
       "      <td>-0.023148</td>\n",
       "      <td>-0.052473</td>\n",
       "      <td>-0.015469</td>\n",
       "      <td>-0.032383</td>\n",
       "      <td>0.033894</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.010519</td>\n",
       "      <td>0.019488</td>\n",
       "      <td>-0.147944</td>\n",
       "      <td>0.013340</td>\n",
       "      <td>-0.149340</td>\n",
       "      <td>0.033991</td>\n",
       "      <td>0.094070</td>\n",
       "      <td>-0.018411</td>\n",
       "      <td>0.079660</td>\n",
       "      <td>-0.138843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cheat</th>\n",
       "      <td>-0.094813</td>\n",
       "      <td>0.024667</td>\n",
       "      <td>0.073189</td>\n",
       "      <td>0.037856</td>\n",
       "      <td>-0.021731</td>\n",
       "      <td>-0.033310</td>\n",
       "      <td>0.233348</td>\n",
       "      <td>0.054941</td>\n",
       "      <td>0.032478</td>\n",
       "      <td>0.066331</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.087263</td>\n",
       "      <td>-0.013366</td>\n",
       "      <td>-0.049529</td>\n",
       "      <td>0.071770</td>\n",
       "      <td>0.014248</td>\n",
       "      <td>0.108391</td>\n",
       "      <td>-0.117653</td>\n",
       "      <td>-0.001598</td>\n",
       "      <td>0.129285</td>\n",
       "      <td>0.019878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stair</th>\n",
       "      <td>0.008491</td>\n",
       "      <td>0.176056</td>\n",
       "      <td>0.079316</td>\n",
       "      <td>-0.106849</td>\n",
       "      <td>-0.078213</td>\n",
       "      <td>0.145118</td>\n",
       "      <td>0.270137</td>\n",
       "      <td>-0.042341</td>\n",
       "      <td>-0.006574</td>\n",
       "      <td>0.132263</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.099036</td>\n",
       "      <td>-0.014422</td>\n",
       "      <td>-0.012541</td>\n",
       "      <td>0.012490</td>\n",
       "      <td>0.069616</td>\n",
       "      <td>-0.013818</td>\n",
       "      <td>-0.015541</td>\n",
       "      <td>-0.097381</td>\n",
       "      <td>0.027210</td>\n",
       "      <td>-0.053017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yes_please</th>\n",
       "      <td>0.126875</td>\n",
       "      <td>-0.064710</td>\n",
       "      <td>0.039775</td>\n",
       "      <td>0.013500</td>\n",
       "      <td>0.143588</td>\n",
       "      <td>0.014899</td>\n",
       "      <td>0.109955</td>\n",
       "      <td>-0.072418</td>\n",
       "      <td>0.247959</td>\n",
       "      <td>0.038823</td>\n",
       "      <td>...</td>\n",
       "      <td>0.013323</td>\n",
       "      <td>0.058483</td>\n",
       "      <td>0.082960</td>\n",
       "      <td>-0.026721</td>\n",
       "      <td>-0.162638</td>\n",
       "      <td>-0.092179</td>\n",
       "      <td>-0.090739</td>\n",
       "      <td>0.017312</td>\n",
       "      <td>0.100419</td>\n",
       "      <td>-0.089760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>town_square</th>\n",
       "      <td>0.093417</td>\n",
       "      <td>-0.125796</td>\n",
       "      <td>0.000875</td>\n",
       "      <td>-0.240906</td>\n",
       "      <td>0.018417</td>\n",
       "      <td>0.051612</td>\n",
       "      <td>0.053485</td>\n",
       "      <td>-0.035484</td>\n",
       "      <td>-0.074278</td>\n",
       "      <td>0.056777</td>\n",
       "      <td>...</td>\n",
       "      <td>0.072435</td>\n",
       "      <td>0.023059</td>\n",
       "      <td>0.088186</td>\n",
       "      <td>-0.020331</td>\n",
       "      <td>-0.240876</td>\n",
       "      <td>0.163422</td>\n",
       "      <td>-0.040497</td>\n",
       "      <td>0.017323</td>\n",
       "      <td>-0.085871</td>\n",
       "      <td>0.023185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>movie_theater</th>\n",
       "      <td>-0.063419</td>\n",
       "      <td>-0.002830</td>\n",
       "      <td>-0.142087</td>\n",
       "      <td>-0.093615</td>\n",
       "      <td>-0.175552</td>\n",
       "      <td>0.012283</td>\n",
       "      <td>-0.031686</td>\n",
       "      <td>0.116937</td>\n",
       "      <td>0.021333</td>\n",
       "      <td>0.176913</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.118533</td>\n",
       "      <td>0.051020</td>\n",
       "      <td>-0.111427</td>\n",
       "      <td>-0.026605</td>\n",
       "      <td>0.054096</td>\n",
       "      <td>-0.039959</td>\n",
       "      <td>-0.035955</td>\n",
       "      <td>-0.066395</td>\n",
       "      <td>0.157689</td>\n",
       "      <td>-0.030838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>not_crazy_about</th>\n",
       "      <td>0.002902</td>\n",
       "      <td>-0.020663</td>\n",
       "      <td>-0.020946</td>\n",
       "      <td>-0.042684</td>\n",
       "      <td>-0.025134</td>\n",
       "      <td>0.172214</td>\n",
       "      <td>0.136586</td>\n",
       "      <td>0.127970</td>\n",
       "      <td>0.012677</td>\n",
       "      <td>-0.051101</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.050716</td>\n",
       "      <td>-0.136015</td>\n",
       "      <td>0.119401</td>\n",
       "      <td>-0.086231</td>\n",
       "      <td>-0.109704</td>\n",
       "      <td>0.190998</td>\n",
       "      <td>0.040013</td>\n",
       "      <td>0.050715</td>\n",
       "      <td>-0.014154</td>\n",
       "      <td>-0.039194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>arepa</th>\n",
       "      <td>-0.156023</td>\n",
       "      <td>0.006913</td>\n",
       "      <td>0.231545</td>\n",
       "      <td>0.190183</td>\n",
       "      <td>0.104871</td>\n",
       "      <td>0.053828</td>\n",
       "      <td>-0.023367</td>\n",
       "      <td>-0.149261</td>\n",
       "      <td>0.036490</td>\n",
       "      <td>0.107712</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.091901</td>\n",
       "      <td>-0.066689</td>\n",
       "      <td>-0.137176</td>\n",
       "      <td>-0.059738</td>\n",
       "      <td>-0.073604</td>\n",
       "      <td>0.076633</td>\n",
       "      <td>-0.200365</td>\n",
       "      <td>-0.003283</td>\n",
       "      <td>0.026638</td>\n",
       "      <td>-0.051232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>no_doubt</th>\n",
       "      <td>-0.255363</td>\n",
       "      <td>-0.158740</td>\n",
       "      <td>-0.020255</td>\n",
       "      <td>0.184975</td>\n",
       "      <td>0.131205</td>\n",
       "      <td>0.017877</td>\n",
       "      <td>-0.053892</td>\n",
       "      <td>0.076563</td>\n",
       "      <td>-0.003248</td>\n",
       "      <td>-0.026703</td>\n",
       "      <td>...</td>\n",
       "      <td>0.009972</td>\n",
       "      <td>0.040705</td>\n",
       "      <td>-0.071313</td>\n",
       "      <td>-0.093857</td>\n",
       "      <td>-0.075304</td>\n",
       "      <td>0.083331</td>\n",
       "      <td>-0.029316</td>\n",
       "      <td>0.086859</td>\n",
       "      <td>0.048552</td>\n",
       "      <td>0.009703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feedback</th>\n",
       "      <td>-0.082663</td>\n",
       "      <td>0.011771</td>\n",
       "      <td>0.071908</td>\n",
       "      <td>0.078901</td>\n",
       "      <td>-0.142023</td>\n",
       "      <td>-0.009928</td>\n",
       "      <td>0.062026</td>\n",
       "      <td>0.007615</td>\n",
       "      <td>0.153321</td>\n",
       "      <td>-0.036233</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.013531</td>\n",
       "      <td>-0.016825</td>\n",
       "      <td>-0.090261</td>\n",
       "      <td>-0.210476</td>\n",
       "      <td>0.047362</td>\n",
       "      <td>0.060007</td>\n",
       "      <td>0.038754</td>\n",
       "      <td>-0.063555</td>\n",
       "      <td>-0.059729</td>\n",
       "      <td>-0.085291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yard_house</th>\n",
       "      <td>-0.196941</td>\n",
       "      <td>-0.116955</td>\n",
       "      <td>0.028073</td>\n",
       "      <td>-0.052867</td>\n",
       "      <td>0.068740</td>\n",
       "      <td>0.086905</td>\n",
       "      <td>0.030740</td>\n",
       "      <td>-0.092705</td>\n",
       "      <td>0.114199</td>\n",
       "      <td>-0.054370</td>\n",
       "      <td>...</td>\n",
       "      <td>0.070938</td>\n",
       "      <td>0.034079</td>\n",
       "      <td>-0.002859</td>\n",
       "      <td>0.149076</td>\n",
       "      <td>-0.109358</td>\n",
       "      <td>-0.011512</td>\n",
       "      <td>0.014135</td>\n",
       "      <td>0.005862</td>\n",
       "      <td>0.008847</td>\n",
       "      <td>-0.111297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>liquid</th>\n",
       "      <td>-0.018640</td>\n",
       "      <td>0.126085</td>\n",
       "      <td>0.043756</td>\n",
       "      <td>0.152486</td>\n",
       "      <td>-0.009277</td>\n",
       "      <td>-0.027216</td>\n",
       "      <td>0.128157</td>\n",
       "      <td>0.020778</td>\n",
       "      <td>-0.113102</td>\n",
       "      <td>-0.066195</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005587</td>\n",
       "      <td>0.081051</td>\n",
       "      <td>0.036859</td>\n",
       "      <td>-0.033761</td>\n",
       "      <td>-0.086981</td>\n",
       "      <td>-0.004796</td>\n",
       "      <td>0.041900</td>\n",
       "      <td>0.200842</td>\n",
       "      <td>0.157308</td>\n",
       "      <td>-0.069588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>niece</th>\n",
       "      <td>-0.139134</td>\n",
       "      <td>0.058908</td>\n",
       "      <td>0.109213</td>\n",
       "      <td>-0.073169</td>\n",
       "      <td>0.058777</td>\n",
       "      <td>0.130696</td>\n",
       "      <td>0.015432</td>\n",
       "      <td>-0.116950</td>\n",
       "      <td>0.052314</td>\n",
       "      <td>0.109274</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.156502</td>\n",
       "      <td>0.099823</td>\n",
       "      <td>-0.046160</td>\n",
       "      <td>0.042264</td>\n",
       "      <td>0.046920</td>\n",
       "      <td>0.148358</td>\n",
       "      <td>-0.156235</td>\n",
       "      <td>0.038355</td>\n",
       "      <td>0.093584</td>\n",
       "      <td>0.309919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>go_wrong</th>\n",
       "      <td>-0.143365</td>\n",
       "      <td>0.034696</td>\n",
       "      <td>0.048508</td>\n",
       "      <td>-0.092662</td>\n",
       "      <td>-0.177424</td>\n",
       "      <td>0.037748</td>\n",
       "      <td>0.095170</td>\n",
       "      <td>-0.077534</td>\n",
       "      <td>0.081717</td>\n",
       "      <td>-0.104574</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.198626</td>\n",
       "      <td>0.091984</td>\n",
       "      <td>-0.067394</td>\n",
       "      <td>0.118116</td>\n",
       "      <td>-0.129116</td>\n",
       "      <td>0.117930</td>\n",
       "      <td>0.016109</td>\n",
       "      <td>-0.086500</td>\n",
       "      <td>0.026070</td>\n",
       "      <td>-0.048419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hassle</th>\n",
       "      <td>-0.016649</td>\n",
       "      <td>-0.180875</td>\n",
       "      <td>-0.153633</td>\n",
       "      <td>0.038115</td>\n",
       "      <td>0.012836</td>\n",
       "      <td>0.056218</td>\n",
       "      <td>0.091712</td>\n",
       "      <td>-0.006957</td>\n",
       "      <td>0.000859</td>\n",
       "      <td>-0.116643</td>\n",
       "      <td>...</td>\n",
       "      <td>0.071548</td>\n",
       "      <td>0.062715</td>\n",
       "      <td>0.090629</td>\n",
       "      <td>0.037725</td>\n",
       "      <td>0.190041</td>\n",
       "      <td>0.176918</td>\n",
       "      <td>-0.035664</td>\n",
       "      <td>-0.117363</td>\n",
       "      <td>0.015967</td>\n",
       "      <td>-0.187644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kim</th>\n",
       "      <td>0.042883</td>\n",
       "      <td>-0.210300</td>\n",
       "      <td>-0.178461</td>\n",
       "      <td>0.013505</td>\n",
       "      <td>0.016396</td>\n",
       "      <td>-0.033317</td>\n",
       "      <td>-0.131029</td>\n",
       "      <td>-0.042195</td>\n",
       "      <td>0.094122</td>\n",
       "      <td>-0.017528</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.271434</td>\n",
       "      <td>-0.120926</td>\n",
       "      <td>-0.054424</td>\n",
       "      <td>-0.033306</td>\n",
       "      <td>-0.087826</td>\n",
       "      <td>-0.180287</td>\n",
       "      <td>0.027707</td>\n",
       "      <td>0.154617</td>\n",
       "      <td>0.063017</td>\n",
       "      <td>-0.024790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>informative</th>\n",
       "      <td>-0.231991</td>\n",
       "      <td>-0.249666</td>\n",
       "      <td>0.051581</td>\n",
       "      <td>-0.029201</td>\n",
       "      <td>-0.106907</td>\n",
       "      <td>-0.028838</td>\n",
       "      <td>0.100395</td>\n",
       "      <td>0.068753</td>\n",
       "      <td>0.119923</td>\n",
       "      <td>-0.083416</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.174859</td>\n",
       "      <td>0.061816</td>\n",
       "      <td>-0.097941</td>\n",
       "      <td>-0.140254</td>\n",
       "      <td>-0.248227</td>\n",
       "      <td>0.157608</td>\n",
       "      <td>0.138603</td>\n",
       "      <td>0.096336</td>\n",
       "      <td>0.067911</td>\n",
       "      <td>-0.077050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cash_register</th>\n",
       "      <td>-0.113195</td>\n",
       "      <td>0.025717</td>\n",
       "      <td>0.184137</td>\n",
       "      <td>0.162436</td>\n",
       "      <td>-0.169995</td>\n",
       "      <td>-0.031457</td>\n",
       "      <td>0.299088</td>\n",
       "      <td>-0.084213</td>\n",
       "      <td>-0.106233</td>\n",
       "      <td>0.098409</td>\n",
       "      <td>...</td>\n",
       "      <td>0.028194</td>\n",
       "      <td>-0.016097</td>\n",
       "      <td>-0.152971</td>\n",
       "      <td>-0.108154</td>\n",
       "      <td>-0.087352</td>\n",
       "      <td>-0.137902</td>\n",
       "      <td>-0.023637</td>\n",
       "      <td>0.026346</td>\n",
       "      <td>0.034069</td>\n",
       "      <td>0.050245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>edinburgh</th>\n",
       "      <td>-0.081377</td>\n",
       "      <td>0.132259</td>\n",
       "      <td>-0.042254</td>\n",
       "      <td>-0.269429</td>\n",
       "      <td>-0.012426</td>\n",
       "      <td>-0.078470</td>\n",
       "      <td>0.162743</td>\n",
       "      <td>-0.129348</td>\n",
       "      <td>0.038156</td>\n",
       "      <td>0.039203</td>\n",
       "      <td>...</td>\n",
       "      <td>0.080404</td>\n",
       "      <td>-0.056350</td>\n",
       "      <td>0.041102</td>\n",
       "      <td>0.024014</td>\n",
       "      <td>-0.082455</td>\n",
       "      <td>0.033730</td>\n",
       "      <td>-0.006149</td>\n",
       "      <td>0.063169</td>\n",
       "      <td>-0.184351</td>\n",
       "      <td>-0.045218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seek</th>\n",
       "      <td>-0.025560</td>\n",
       "      <td>-0.138242</td>\n",
       "      <td>0.001347</td>\n",
       "      <td>0.051488</td>\n",
       "      <td>-0.017233</td>\n",
       "      <td>0.032183</td>\n",
       "      <td>0.001172</td>\n",
       "      <td>0.077071</td>\n",
       "      <td>0.125390</td>\n",
       "      <td>0.008605</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.033683</td>\n",
       "      <td>0.044872</td>\n",
       "      <td>0.115288</td>\n",
       "      <td>0.048486</td>\n",
       "      <td>0.031469</td>\n",
       "      <td>0.024710</td>\n",
       "      <td>-0.081970</td>\n",
       "      <td>0.009596</td>\n",
       "      <td>0.038987</td>\n",
       "      <td>-0.127455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>about_15</th>\n",
       "      <td>0.146417</td>\n",
       "      <td>0.016803</td>\n",
       "      <td>-0.108962</td>\n",
       "      <td>0.026889</td>\n",
       "      <td>0.027089</td>\n",
       "      <td>-0.095039</td>\n",
       "      <td>0.190872</td>\n",
       "      <td>-0.097652</td>\n",
       "      <td>0.075353</td>\n",
       "      <td>0.075652</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.097098</td>\n",
       "      <td>0.047420</td>\n",
       "      <td>0.046161</td>\n",
       "      <td>-0.147371</td>\n",
       "      <td>-0.060293</td>\n",
       "      <td>0.052256</td>\n",
       "      <td>-0.017365</td>\n",
       "      <td>0.146808</td>\n",
       "      <td>0.031839</td>\n",
       "      <td>-0.156082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5_5</th>\n",
       "      <td>-0.126489</td>\n",
       "      <td>-0.157504</td>\n",
       "      <td>-0.082341</td>\n",
       "      <td>0.201929</td>\n",
       "      <td>0.119957</td>\n",
       "      <td>-0.135224</td>\n",
       "      <td>0.104833</td>\n",
       "      <td>0.004317</td>\n",
       "      <td>0.087903</td>\n",
       "      <td>-0.001731</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.054716</td>\n",
       "      <td>0.022151</td>\n",
       "      <td>0.082819</td>\n",
       "      <td>-0.143014</td>\n",
       "      <td>-0.191793</td>\n",
       "      <td>-0.081282</td>\n",
       "      <td>-0.016158</td>\n",
       "      <td>0.143132</td>\n",
       "      <td>0.000726</td>\n",
       "      <td>-0.202967</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3629 rows × 100 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       0         1         2         3         4         5   \\\n",
       "-PRON-          -0.192540 -0.137421  0.087687 -0.041500 -0.046415  0.062379   \n",
       "be              -0.096318 -0.144332 -0.212839 -0.052480  0.036218  0.219431   \n",
       "the              0.000089 -0.028026 -0.041021  0.111821 -0.085819  0.043335   \n",
       "and             -0.189819 -0.073866  0.065609  0.040589 -0.076059  0.059223   \n",
       "a                0.030527  0.019898 -0.173009  0.110138  0.061045  0.147410   \n",
       "to              -0.184892  0.053593  0.148398 -0.002422 -0.278245  0.127144   \n",
       "have            -0.263948 -0.139392 -0.094716 -0.105031  0.029591  0.186383   \n",
       "of              -0.064054 -0.021256 -0.037077 -0.136387  0.130824  0.011794   \n",
       "for             -0.010958 -0.093656 -0.013771  0.135409  0.151720  0.223458   \n",
       "in              -0.127789 -0.014818 -0.066206 -0.095683  0.045466  0.096631   \n",
       "but             -0.066095 -0.054027 -0.013163 -0.014823  0.035345  0.067532   \n",
       "not             -0.238327 -0.041270 -0.091936 -0.080716  0.024456  0.114123   \n",
       "with            -0.151605 -0.094535  0.031022  0.052123 -0.020034  0.242343   \n",
       "that            -0.102382  0.051184  0.072436  0.008235 -0.062911  0.022180   \n",
       "good            -0.188190 -0.067030 -0.128134 -0.042151  0.127970  0.109065   \n",
       "on              -0.005899 -0.084236  0.072148 -0.039223 -0.083173  0.205746   \n",
       "food            -0.094605 -0.057565  0.019090  0.052923 -0.105340 -0.125611   \n",
       "so              -0.183660 -0.048636 -0.066389 -0.172881 -0.075315  0.127804   \n",
       "this            -0.108534  0.065841  0.105023 -0.070618 -0.065963 -0.033980   \n",
       "get             -0.089895 -0.160190 -0.204633  0.145030  0.118558  0.124394   \n",
       "order           -0.095767 -0.231510  0.041440  0.074546 -0.036252  0.052206   \n",
       "at               0.083019 -0.009276  0.154966 -0.084689  0.097699 -0.075665   \n",
       "go               0.048564 -0.091995  0.026214  0.021247 -0.092897 -0.015113   \n",
       "there           -0.008382 -0.107287 -0.119996 -0.053642 -0.133700 -0.029199   \n",
       "great           -0.117353 -0.018317 -0.026486  0.144878  0.142012  0.094409   \n",
       "like            -0.146573 -0.125287  0.050000 -0.027444  0.021749  0.216425   \n",
       "do_not          -0.204803  0.029435 -0.027627  0.043308 -0.058731 -0.054174   \n",
       "as              -0.141327 -0.184162 -0.011112 -0.077010 -0.049078  0.037249   \n",
       "just            -0.075492 -0.153972  0.198078  0.134122 -0.151163  0.167485   \n",
       "all             -0.194047 -0.017328  0.187311 -0.005889  0.019718 -0.101647   \n",
       "...                   ...       ...       ...       ...       ...       ...   \n",
       "soupe           -0.022476 -0.118028 -0.036387 -0.042614  0.003509 -0.047582   \n",
       "caviar          -0.135327  0.034995 -0.024154 -0.076070  0.078668  0.064858   \n",
       "too_greasy      -0.022927  0.011734 -0.067743  0.087502 -0.034038  0.042902   \n",
       "turkey_burger   -0.210112 -0.075608  0.123027 -0.144845 -0.065669  0.159550   \n",
       "about_8          0.074899  0.066140 -0.087793 -0.110130  0.096991 -0.022319   \n",
       "popcorn_chicken -0.051711 -0.070500  0.237497  0.128638 -0.050679 -0.015431   \n",
       "theatre          0.021344  0.009175  0.140688 -0.049587 -0.126946  0.052697   \n",
       "star_rating     -0.073516 -0.214689  0.126476 -0.117121  0.050298  0.124286   \n",
       "about_$_10       0.081247 -0.105166  0.159298  0.339183  0.169080 -0.023148   \n",
       "cheat           -0.094813  0.024667  0.073189  0.037856 -0.021731 -0.033310   \n",
       "stair            0.008491  0.176056  0.079316 -0.106849 -0.078213  0.145118   \n",
       "yes_please       0.126875 -0.064710  0.039775  0.013500  0.143588  0.014899   \n",
       "town_square      0.093417 -0.125796  0.000875 -0.240906  0.018417  0.051612   \n",
       "movie_theater   -0.063419 -0.002830 -0.142087 -0.093615 -0.175552  0.012283   \n",
       "not_crazy_about  0.002902 -0.020663 -0.020946 -0.042684 -0.025134  0.172214   \n",
       "arepa           -0.156023  0.006913  0.231545  0.190183  0.104871  0.053828   \n",
       "no_doubt        -0.255363 -0.158740 -0.020255  0.184975  0.131205  0.017877   \n",
       "feedback        -0.082663  0.011771  0.071908  0.078901 -0.142023 -0.009928   \n",
       "yard_house      -0.196941 -0.116955  0.028073 -0.052867  0.068740  0.086905   \n",
       "liquid          -0.018640  0.126085  0.043756  0.152486 -0.009277 -0.027216   \n",
       "niece           -0.139134  0.058908  0.109213 -0.073169  0.058777  0.130696   \n",
       "go_wrong        -0.143365  0.034696  0.048508 -0.092662 -0.177424  0.037748   \n",
       "hassle          -0.016649 -0.180875 -0.153633  0.038115  0.012836  0.056218   \n",
       "kim              0.042883 -0.210300 -0.178461  0.013505  0.016396 -0.033317   \n",
       "informative     -0.231991 -0.249666  0.051581 -0.029201 -0.106907 -0.028838   \n",
       "cash_register   -0.113195  0.025717  0.184137  0.162436 -0.169995 -0.031457   \n",
       "edinburgh       -0.081377  0.132259 -0.042254 -0.269429 -0.012426 -0.078470   \n",
       "seek            -0.025560 -0.138242  0.001347  0.051488 -0.017233  0.032183   \n",
       "about_15         0.146417  0.016803 -0.108962  0.026889  0.027089 -0.095039   \n",
       "5_5             -0.126489 -0.157504 -0.082341  0.201929  0.119957 -0.135224   \n",
       "\n",
       "                       6         7         8         9     ...           90  \\\n",
       "-PRON-           0.016884 -0.103175  0.140363  0.088608    ...     0.015603   \n",
       "be               0.000228 -0.034399  0.202455 -0.034905    ...     0.006305   \n",
       "the             -0.056748 -0.131266  0.042512  0.022696    ...    -0.188833   \n",
       "and              0.021162 -0.162560  0.112582 -0.030728    ...    -0.106540   \n",
       "a                0.076303 -0.185216 -0.074776 -0.000041    ...    -0.015731   \n",
       "to              -0.046509 -0.088389  0.231082  0.091001    ...     0.131239   \n",
       "have             0.089885 -0.073477  0.127601 -0.038056    ...    -0.042434   \n",
       "of              -0.070115 -0.086504  0.034637  0.240778    ...    -0.142515   \n",
       "for              0.132373  0.050078  0.031994  0.018673    ...    -0.091269   \n",
       "in               0.072912  0.010753  0.061598  0.327738    ...    -0.108266   \n",
       "but              0.114754 -0.133493  0.132913 -0.042661    ...    -0.021344   \n",
       "not              0.166103 -0.062798  0.103472 -0.115738    ...     0.057384   \n",
       "with             0.020790 -0.083308 -0.038762  0.030932    ...    -0.114223   \n",
       "that             0.005541 -0.065420  0.128774 -0.036637    ...    -0.083903   \n",
       "good             0.065108 -0.068333  0.096048 -0.027358    ...    -0.113426   \n",
       "on               0.260500  0.025992 -0.087802  0.113882    ...    -0.134531   \n",
       "food             0.026780  0.035995  0.088988  0.218870    ...     0.052335   \n",
       "so               0.082498 -0.145854  0.085634  0.055793    ...     0.066474   \n",
       "this             0.057006  0.035344 -0.072082  0.004064    ...     0.096331   \n",
       "get              0.054298 -0.095441 -0.004761  0.035745    ...    -0.103003   \n",
       "order            0.049430 -0.073733 -0.031316 -0.016103    ...    -0.051730   \n",
       "at               0.232719  0.066325  0.010844  0.178534    ...    -0.035956   \n",
       "go               0.095624 -0.149511  0.115619  0.056114    ...    -0.164015   \n",
       "there            0.304989  0.083237 -0.030373  0.079556    ...     0.024329   \n",
       "great            0.014710 -0.114920  0.112542  0.031354    ...    -0.214965   \n",
       "like             0.054879  0.024403  0.066836  0.046442    ...    -0.221343   \n",
       "do_not           0.023838 -0.067478  0.064517  0.018739    ...     0.008481   \n",
       "as               0.023733 -0.118470  0.044099  0.045384    ...    -0.010985   \n",
       "just             0.094396 -0.093465  0.172112  0.076595    ...     0.029857   \n",
       "all              0.101547 -0.101570  0.018934  0.133588    ...     0.068676   \n",
       "...                   ...       ...       ...       ...    ...          ...   \n",
       "soupe            0.136152 -0.118974 -0.090659 -0.025446    ...    -0.226941   \n",
       "caviar           0.001998 -0.001652 -0.105238  0.018555    ...    -0.022385   \n",
       "too_greasy      -0.217221 -0.036918  0.050670 -0.025139    ...     0.033056   \n",
       "turkey_burger    0.038045 -0.097440  0.047505 -0.112820    ...    -0.033055   \n",
       "about_8          0.102327  0.001946 -0.017980  0.154447    ...     0.060591   \n",
       "popcorn_chicken -0.071123  0.022381  0.091113  0.078115    ...    -0.027268   \n",
       "theatre          0.235300 -0.051903  0.099603  0.121937    ...    -0.049365   \n",
       "star_rating     -0.033591 -0.089336  0.108219 -0.004200    ...    -0.054798   \n",
       "about_$_10      -0.052473 -0.015469 -0.032383  0.033894    ...    -0.010519   \n",
       "cheat            0.233348  0.054941  0.032478  0.066331    ...    -0.087263   \n",
       "stair            0.270137 -0.042341 -0.006574  0.132263    ...    -0.099036   \n",
       "yes_please       0.109955 -0.072418  0.247959  0.038823    ...     0.013323   \n",
       "town_square      0.053485 -0.035484 -0.074278  0.056777    ...     0.072435   \n",
       "movie_theater   -0.031686  0.116937  0.021333  0.176913    ...    -0.118533   \n",
       "not_crazy_about  0.136586  0.127970  0.012677 -0.051101    ...    -0.050716   \n",
       "arepa           -0.023367 -0.149261  0.036490  0.107712    ...    -0.091901   \n",
       "no_doubt        -0.053892  0.076563 -0.003248 -0.026703    ...     0.009972   \n",
       "feedback         0.062026  0.007615  0.153321 -0.036233    ...    -0.013531   \n",
       "yard_house       0.030740 -0.092705  0.114199 -0.054370    ...     0.070938   \n",
       "liquid           0.128157  0.020778 -0.113102 -0.066195    ...     0.005587   \n",
       "niece            0.015432 -0.116950  0.052314  0.109274    ...    -0.156502   \n",
       "go_wrong         0.095170 -0.077534  0.081717 -0.104574    ...    -0.198626   \n",
       "hassle           0.091712 -0.006957  0.000859 -0.116643    ...     0.071548   \n",
       "kim             -0.131029 -0.042195  0.094122 -0.017528    ...    -0.271434   \n",
       "informative      0.100395  0.068753  0.119923 -0.083416    ...    -0.174859   \n",
       "cash_register    0.299088 -0.084213 -0.106233  0.098409    ...     0.028194   \n",
       "edinburgh        0.162743 -0.129348  0.038156  0.039203    ...     0.080404   \n",
       "seek             0.001172  0.077071  0.125390  0.008605    ...    -0.033683   \n",
       "about_15         0.190872 -0.097652  0.075353  0.075652    ...    -0.097098   \n",
       "5_5              0.104833  0.004317  0.087903 -0.001731    ...    -0.054716   \n",
       "\n",
       "                       91        92        93        94        95        96  \\\n",
       "-PRON-          -0.126255 -0.051399  0.037474  0.139312  0.042000 -0.059149   \n",
       "be               0.000102  0.030234  0.043527 -0.017617  0.009483 -0.145317   \n",
       "the             -0.068218 -0.112790  0.042619 -0.024181  0.014776 -0.080565   \n",
       "and              0.071831 -0.049181  0.066906 -0.054017 -0.077172 -0.196286   \n",
       "a                0.120600 -0.101515  0.006418 -0.044200 -0.026002 -0.085152   \n",
       "to               0.017966 -0.044981  0.002161  0.141389 -0.029075 -0.197160   \n",
       "have             0.003974 -0.080431 -0.017601  0.019746  0.111616 -0.128615   \n",
       "of               0.128248 -0.085780  0.114970 -0.082709  0.076574 -0.083384   \n",
       "for              0.116898  0.030665 -0.040892  0.132972 -0.132993 -0.290694   \n",
       "in               0.009744 -0.000948  0.109708  0.077226  0.040942 -0.131442   \n",
       "but             -0.036936 -0.045068  0.127355  0.098319 -0.041496 -0.162874   \n",
       "not             -0.056605  0.091968  0.078609  0.035846 -0.090300  0.067451   \n",
       "with             0.168517  0.001207  0.102143  0.013646 -0.129282 -0.103153   \n",
       "that            -0.016001 -0.075827  0.135004  0.061166  0.068840 -0.101208   \n",
       "good            -0.200908 -0.002371 -0.086590 -0.028552  0.098042  0.027771   \n",
       "on               0.032244 -0.011631  0.016936  0.081113  0.130330 -0.197432   \n",
       "food            -0.060964  0.073406 -0.170049  0.042704  0.177785 -0.025385   \n",
       "so              -0.010231  0.013490  0.105681  0.002420  0.025109 -0.201466   \n",
       "this            -0.084680 -0.015909  0.048753  0.116532  0.058992 -0.022128   \n",
       "get             -0.002285  0.077270  0.122984  0.155547  0.094730 -0.203995   \n",
       "order            0.077677  0.050358  0.009253 -0.002400  0.193037 -0.060212   \n",
       "at               0.047810 -0.118697  0.056097  0.154180  0.023770 -0.113843   \n",
       "go               0.002612  0.245725 -0.016533  0.021000  0.242736 -0.064401   \n",
       "there            0.071463  0.119142 -0.080713  0.001862 -0.009744 -0.183875   \n",
       "great            0.072440  0.065399 -0.138333 -0.238280  0.055685  0.005077   \n",
       "like             0.021544 -0.080988  0.149703 -0.023309  0.086434 -0.059255   \n",
       "do_not          -0.116913  0.088326  0.137177  0.030670 -0.000638 -0.056274   \n",
       "as              -0.070742 -0.099017  0.078669 -0.066038  0.064837 -0.204999   \n",
       "just             0.039638  0.003459  0.001979 -0.003595  0.060887 -0.128413   \n",
       "all              0.148213  0.002361  0.141743 -0.164167  0.123084  0.011680   \n",
       "...                   ...       ...       ...       ...       ...       ...   \n",
       "soupe            0.035727  0.093376 -0.036261 -0.101395 -0.056403 -0.146233   \n",
       "caviar           0.087690 -0.013808 -0.042190 -0.189940  0.142276 -0.064569   \n",
       "too_greasy      -0.150457  0.030881 -0.085927 -0.056622  0.019331  0.114762   \n",
       "turkey_burger   -0.091538 -0.240900 -0.220910 -0.092483  0.021548 -0.027130   \n",
       "about_8          0.142302 -0.063091 -0.093958 -0.028988  0.018035 -0.131597   \n",
       "popcorn_chicken -0.141960 -0.050201  0.016049 -0.222791 -0.193952 -0.109775   \n",
       "theatre         -0.096886  0.088006  0.099423 -0.134382  0.042007  0.002219   \n",
       "star_rating     -0.169646 -0.097593  0.003510 -0.103876  0.105514 -0.118692   \n",
       "about_$_10       0.019488 -0.147944  0.013340 -0.149340  0.033991  0.094070   \n",
       "cheat           -0.013366 -0.049529  0.071770  0.014248  0.108391 -0.117653   \n",
       "stair           -0.014422 -0.012541  0.012490  0.069616 -0.013818 -0.015541   \n",
       "yes_please       0.058483  0.082960 -0.026721 -0.162638 -0.092179 -0.090739   \n",
       "town_square      0.023059  0.088186 -0.020331 -0.240876  0.163422 -0.040497   \n",
       "movie_theater    0.051020 -0.111427 -0.026605  0.054096 -0.039959 -0.035955   \n",
       "not_crazy_about -0.136015  0.119401 -0.086231 -0.109704  0.190998  0.040013   \n",
       "arepa           -0.066689 -0.137176 -0.059738 -0.073604  0.076633 -0.200365   \n",
       "no_doubt         0.040705 -0.071313 -0.093857 -0.075304  0.083331 -0.029316   \n",
       "feedback        -0.016825 -0.090261 -0.210476  0.047362  0.060007  0.038754   \n",
       "yard_house       0.034079 -0.002859  0.149076 -0.109358 -0.011512  0.014135   \n",
       "liquid           0.081051  0.036859 -0.033761 -0.086981 -0.004796  0.041900   \n",
       "niece            0.099823 -0.046160  0.042264  0.046920  0.148358 -0.156235   \n",
       "go_wrong         0.091984 -0.067394  0.118116 -0.129116  0.117930  0.016109   \n",
       "hassle           0.062715  0.090629  0.037725  0.190041  0.176918 -0.035664   \n",
       "kim             -0.120926 -0.054424 -0.033306 -0.087826 -0.180287  0.027707   \n",
       "informative      0.061816 -0.097941 -0.140254 -0.248227  0.157608  0.138603   \n",
       "cash_register   -0.016097 -0.152971 -0.108154 -0.087352 -0.137902 -0.023637   \n",
       "edinburgh       -0.056350  0.041102  0.024014 -0.082455  0.033730 -0.006149   \n",
       "seek             0.044872  0.115288  0.048486  0.031469  0.024710 -0.081970   \n",
       "about_15         0.047420  0.046161 -0.147371 -0.060293  0.052256 -0.017365   \n",
       "5_5              0.022151  0.082819 -0.143014 -0.191793 -0.081282 -0.016158   \n",
       "\n",
       "                       97        98        99  \n",
       "-PRON-           0.078348 -0.050734 -0.068732  \n",
       "be               0.228525 -0.069701 -0.114176  \n",
       "the              0.028031 -0.114328  0.064669  \n",
       "and              0.115795 -0.091203  0.083549  \n",
       "a                0.075556  0.042961 -0.011100  \n",
       "to              -0.052363 -0.029980 -0.062437  \n",
       "have             0.125177 -0.054404  0.176189  \n",
       "of               0.117512 -0.182946 -0.000966  \n",
       "for              0.164789 -0.119832 -0.133160  \n",
       "in              -0.055605 -0.031663 -0.078908  \n",
       "but              0.141484 -0.122386 -0.212305  \n",
       "not              0.088992 -0.082238 -0.216122  \n",
       "with             0.054679 -0.032169  0.095618  \n",
       "that            -0.042464 -0.104981 -0.136647  \n",
       "good            -0.002126  0.019668 -0.142265  \n",
       "on               0.109674  0.067194  0.069555  \n",
       "food            -0.051239 -0.159380 -0.175729  \n",
       "so              -0.063068 -0.132008 -0.203403  \n",
       "this            -0.092712 -0.167243 -0.204690  \n",
       "get             -0.077664  0.046396  0.044063  \n",
       "order            0.041352  0.068094  0.041941  \n",
       "at              -0.051588 -0.062524 -0.109468  \n",
       "go              -0.078001  0.005967 -0.004110  \n",
       "there           -0.009156 -0.153777 -0.019262  \n",
       "great            0.012492 -0.005923 -0.070824  \n",
       "like             0.038443 -0.084650 -0.064201  \n",
       "do_not           0.001611 -0.168155 -0.020512  \n",
       "as               0.165826 -0.144399 -0.207110  \n",
       "just            -0.020340 -0.081539 -0.192929  \n",
       "all              0.053275 -0.155196 -0.088775  \n",
       "...                   ...       ...       ...  \n",
       "soupe           -0.097134  0.002730 -0.030946  \n",
       "caviar           0.083470 -0.128207 -0.148390  \n",
       "too_greasy       0.128876  0.078853  0.012402  \n",
       "turkey_burger   -0.128636 -0.086874  0.102681  \n",
       "about_8          0.201788 -0.019231 -0.012712  \n",
       "popcorn_chicken  0.015500  0.071373 -0.027344  \n",
       "theatre         -0.004592  0.161482 -0.063428  \n",
       "star_rating      0.039148 -0.041415 -0.075414  \n",
       "about_$_10      -0.018411  0.079660 -0.138843  \n",
       "cheat           -0.001598  0.129285  0.019878  \n",
       "stair           -0.097381  0.027210 -0.053017  \n",
       "yes_please       0.017312  0.100419 -0.089760  \n",
       "town_square      0.017323 -0.085871  0.023185  \n",
       "movie_theater   -0.066395  0.157689 -0.030838  \n",
       "not_crazy_about  0.050715 -0.014154 -0.039194  \n",
       "arepa           -0.003283  0.026638 -0.051232  \n",
       "no_doubt         0.086859  0.048552  0.009703  \n",
       "feedback        -0.063555 -0.059729 -0.085291  \n",
       "yard_house       0.005862  0.008847 -0.111297  \n",
       "liquid           0.200842  0.157308 -0.069588  \n",
       "niece            0.038355  0.093584  0.309919  \n",
       "go_wrong        -0.086500  0.026070 -0.048419  \n",
       "hassle          -0.117363  0.015967 -0.187644  \n",
       "kim              0.154617  0.063017 -0.024790  \n",
       "informative      0.096336  0.067911 -0.077050  \n",
       "cash_register    0.026346  0.034069  0.050245  \n",
       "edinburgh        0.063169 -0.184351 -0.045218  \n",
       "seek             0.009596  0.038987 -0.127455  \n",
       "about_15         0.146808  0.031839 -0.156082  \n",
       "5_5              0.143132  0.000726 -0.202967  \n",
       "\n",
       "[3629 rows x 100 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# build a list of the terms, integer indices,\n",
    "# and term counts from the food2vec model vocabulary\n",
    "ordered_vocab = [(term, voc.index, voc.count)\n",
    "                 for term, voc in food2vec.wv.vocab.items()]\n",
    "\n",
    "def sort_func(tup):\n",
    "    term, indec, count = tup\n",
    "    return -count\n",
    "\n",
    "# sort by the term counts, so the most common terms appear first\n",
    "ordered_vocab = sorted(ordered_vocab, key=sort_func)\n",
    "\n",
    "# unzip the terms, integer indices, and counts into separate lists\n",
    "ordered_terms, term_indices, term_counts = zip(*ordered_vocab)\n",
    "\n",
    "# create a DataFrame with the food2vec vectors as data,\n",
    "# and the terms as row labels\n",
    "word_vectors = pd.DataFrame(food2vec.wv.vectors_norm[term_indices, :],\n",
    "                            index=ordered_terms)\n",
    "\n",
    "word_vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_related_terms(token, topn=10):\n",
    "    \"\"\"\n",
    "    look up the topn most similar terms to token\n",
    "    and print them as a formatted list\n",
    "    \"\"\"\n",
    "\n",
    "    for word, similarity in food2vec.wv.most_similar(positive=[token], topn=topn):\n",
    "\n",
    "        print(u'{:20} {}'.format(word, round(similarity, 3)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "slider               0.601\n",
      "sandwich             0.547\n",
      "wing                 0.526\n",
      "bacon                0.526\n",
      "turkey_burger        0.525\n",
      "fry                  0.508\n",
      "filet                0.499\n",
      "cheeseburger         0.495\n",
      "lettuce_tomato       0.486\n",
      "tater_tot            0.472\n"
     ]
    }
   ],
   "source": [
    "get_related_terms(u'burger')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pepperoni            0.602\n",
      "pie                  0.575\n",
      "salami               0.519\n",
      "bbq_chicken          0.515\n",
      "thin_crust           0.507\n",
      "mozzarella           0.495\n",
      "wing                 0.49\n",
      "lasagna              0.475\n",
      "cheeseburger         0.47\n",
      "burger               0.467\n"
     ]
    }
   ],
   "source": [
    "get_related_terms(u'pizza')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tuesday              0.572\n",
      "during_happy_hour    0.551\n",
      "10_p.m.              0.549\n",
      "hh                   0.534\n",
      "happy_hour_special   0.524\n",
      "every_day            0.517\n",
      "wednesday            0.504\n",
      "friday               0.497\n",
      "thursday             0.494\n",
      "7_p.m.               0.494\n"
     ]
    }
   ],
   "source": [
    "get_related_terms(u'happy_hour')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "marinara             0.587\n",
      "penne                0.563\n",
      "bolognese            0.561\n",
      "mushroom             0.544\n",
      "spinach              0.539\n",
      "cream_sauce          0.523\n",
      "arugula              0.519\n",
      "lasagna              0.516\n",
      "risotto              0.506\n",
      "sausage              0.497\n",
      "parmesan             0.496\n",
      "mozzarella           0.489\n",
      "carbonara            0.483\n",
      "tomato               0.483\n",
      "soup                 0.482\n",
      "spaghetti            0.482\n",
      "veal                 0.479\n",
      "pesto                0.468\n",
      "lamb                 0.467\n",
      "pizza                0.463\n"
     ]
    }
   ],
   "source": [
    "get_related_terms(u'pasta', topn=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "def word_algebra(add=[], subtract=[], topn=1):\n",
    "    \"\"\"\n",
    "    combine the vectors associated with the words provided\n",
    "    in add= and subtract=, look up the topn most similar\n",
    "    terms to the combined vector, and print the result(s)\n",
    "    \"\"\"\n",
    "    answers = food2vec.wv.most_similar(positive=add, negative=subtract, topn=topn)\n",
    "    \n",
    "    for term, similarity in answers:\n",
    "        print( term)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "brunch\n"
     ]
    }
   ],
   "source": [
    "word_algebra(add=[u'breakfast', u'lunch'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dinner\n"
     ]
    }
   ],
   "source": [
    "word_algebra(add=[u'lunch', u'night'], subtract=[u'day'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "carnita\n"
     ]
    }
   ],
   "source": [
    "word_algebra(add=[u'taco', u'chinese'], subtract=[u'mexican'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tortilla\n"
     ]
    }
   ],
   "source": [
    "word_algebra(add=[u'bun', u'mexican'], subtract=[u'american'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "desert\n"
     ]
    }
   ],
   "source": [
    "word_algebra(add=[u'filet_mignon', u'seafood'], subtract=[u'beef'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hazelnut\n"
     ]
    }
   ],
   "source": [
    "word_algebra(add=[u'coffee', u'snack'], subtract=[u'drink'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fast_food_joint\n"
     ]
    }
   ],
   "source": [
    "word_algebra(add=[u'burger', u'fine_dining'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.manifold import TSNE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "tsne_input = word_vectors.drop(spacy.lang.en.STOP_WORDS, errors=u'ignore')\n",
    "tsne_input = tsne_input.head(5000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>-0.137421</td>\n",
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       "      <td>0.140363</td>\n",
       "      <td>0.088608</td>\n",
       "      <td>...</td>\n",
       "      <td>0.015603</td>\n",
       "      <td>-0.126255</td>\n",
       "      <td>-0.051399</td>\n",
       "      <td>0.037474</td>\n",
       "      <td>0.139312</td>\n",
       "      <td>0.042000</td>\n",
       "      <td>-0.059149</td>\n",
       "      <td>0.078348</td>\n",
       "      <td>-0.050734</td>\n",
       "      <td>-0.068732</td>\n",
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       "    <tr>\n",
       "      <th>good</th>\n",
       "      <td>-0.188190</td>\n",
       "      <td>-0.067030</td>\n",
       "      <td>-0.128134</td>\n",
       "      <td>-0.042151</td>\n",
       "      <td>0.127970</td>\n",
       "      <td>0.109065</td>\n",
       "      <td>0.065108</td>\n",
       "      <td>-0.068333</td>\n",
       "      <td>0.096048</td>\n",
       "      <td>-0.027358</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.113426</td>\n",
       "      <td>-0.200908</td>\n",
       "      <td>-0.002371</td>\n",
       "      <td>-0.086590</td>\n",
       "      <td>-0.028552</td>\n",
       "      <td>0.098042</td>\n",
       "      <td>0.027771</td>\n",
       "      <td>-0.002126</td>\n",
       "      <td>0.019668</td>\n",
       "      <td>-0.142265</td>\n",
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       "    <tr>\n",
       "      <th>food</th>\n",
       "      <td>-0.094605</td>\n",
       "      <td>-0.057565</td>\n",
       "      <td>0.019090</td>\n",
       "      <td>0.052923</td>\n",
       "      <td>-0.105340</td>\n",
       "      <td>-0.125611</td>\n",
       "      <td>0.026780</td>\n",
       "      <td>0.035995</td>\n",
       "      <td>0.088988</td>\n",
       "      <td>0.218870</td>\n",
       "      <td>...</td>\n",
       "      <td>0.052335</td>\n",
       "      <td>-0.060964</td>\n",
       "      <td>0.073406</td>\n",
       "      <td>-0.170049</td>\n",
       "      <td>0.042704</td>\n",
       "      <td>0.177785</td>\n",
       "      <td>-0.025385</td>\n",
       "      <td>-0.051239</td>\n",
       "      <td>-0.159380</td>\n",
       "      <td>-0.175729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>order</th>\n",
       "      <td>-0.095767</td>\n",
       "      <td>-0.231510</td>\n",
       "      <td>0.041440</td>\n",
       "      <td>0.074546</td>\n",
       "      <td>-0.036252</td>\n",
       "      <td>0.052206</td>\n",
       "      <td>0.049430</td>\n",
       "      <td>-0.073733</td>\n",
       "      <td>-0.031316</td>\n",
       "      <td>-0.016103</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.051730</td>\n",
       "      <td>0.077677</td>\n",
       "      <td>0.050358</td>\n",
       "      <td>0.009253</td>\n",
       "      <td>-0.002400</td>\n",
       "      <td>0.193037</td>\n",
       "      <td>-0.060212</td>\n",
       "      <td>0.041352</td>\n",
       "      <td>0.068094</td>\n",
       "      <td>0.041941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>great</th>\n",
       "      <td>-0.117353</td>\n",
       "      <td>-0.018317</td>\n",
       "      <td>-0.026486</td>\n",
       "      <td>0.144878</td>\n",
       "      <td>0.142012</td>\n",
       "      <td>0.094409</td>\n",
       "      <td>0.014710</td>\n",
       "      <td>-0.114920</td>\n",
       "      <td>0.112542</td>\n",
       "      <td>0.031354</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.214965</td>\n",
       "      <td>0.072440</td>\n",
       "      <td>0.065399</td>\n",
       "      <td>-0.138333</td>\n",
       "      <td>-0.238280</td>\n",
       "      <td>0.055685</td>\n",
       "      <td>0.005077</td>\n",
       "      <td>0.012492</td>\n",
       "      <td>-0.005923</td>\n",
       "      <td>-0.070824</td>\n",
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       "<p>5 rows × 100 columns</p>\n",
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      ],
      "text/plain": [
       "              0         1         2         3         4         5         6   \\\n",
       "-PRON- -0.192540 -0.137421  0.087687 -0.041500 -0.046415  0.062379  0.016884   \n",
       "good   -0.188190 -0.067030 -0.128134 -0.042151  0.127970  0.109065  0.065108   \n",
       "food   -0.094605 -0.057565  0.019090  0.052923 -0.105340 -0.125611  0.026780   \n",
       "order  -0.095767 -0.231510  0.041440  0.074546 -0.036252  0.052206  0.049430   \n",
       "great  -0.117353 -0.018317 -0.026486  0.144878  0.142012  0.094409  0.014710   \n",
       "\n",
       "              7         8         9     ...           90        91        92  \\\n",
       "-PRON- -0.103175  0.140363  0.088608    ...     0.015603 -0.126255 -0.051399   \n",
       "good   -0.068333  0.096048 -0.027358    ...    -0.113426 -0.200908 -0.002371   \n",
       "food    0.035995  0.088988  0.218870    ...     0.052335 -0.060964  0.073406   \n",
       "order  -0.073733 -0.031316 -0.016103    ...    -0.051730  0.077677  0.050358   \n",
       "great  -0.114920  0.112542  0.031354    ...    -0.214965  0.072440  0.065399   \n",
       "\n",
       "              93        94        95        96        97        98        99  \n",
       "-PRON-  0.037474  0.139312  0.042000 -0.059149  0.078348 -0.050734 -0.068732  \n",
       "good   -0.086590 -0.028552  0.098042  0.027771 -0.002126  0.019668 -0.142265  \n",
       "food   -0.170049  0.042704  0.177785 -0.025385 -0.051239 -0.159380 -0.175729  \n",
       "order   0.009253 -0.002400  0.193037 -0.060212  0.041352  0.068094  0.041941  \n",
       "great  -0.138333 -0.238280  0.055685  0.005077  0.012492 -0.005923 -0.070824  \n",
       "\n",
       "[5 rows x 100 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tsne_input.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'os' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-5-871f115754c4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m tsne_filepath = os.path.join(intermediate_directory,\n\u001b[0m\u001b[1;32m      2\u001b[0m                              u'tsne_model')\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m tsne_vectors_filepath = os.path.join(intermediate_directory,\n\u001b[1;32m      5\u001b[0m                                      u'tsne_vectors.npy')\n",
      "\u001b[0;31mNameError\u001b[0m: name 'os' is not defined"
     ]
    }
   ],
   "source": [
    "tsne_filepath = os.path.join(intermediate_directory,\n",
    "                             u'tsne_model')\n",
    "\n",
    "tsne_vectors_filepath = os.path.join(intermediate_directory,\n",
    "                                     u'tsne_vectors.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1min 55s, sys: 9.61 s, total: 2min 5s\n",
      "Wall time: 2min 8s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "if process:\n",
    "    \n",
    "    tsne = TSNE()\n",
    "    tsne_vectors = tsne.fit_transform(tsne_input.values)\n",
    "    \n",
    "    with open(tsne_filepath, 'wb') as f:\n",
    "        pickle.dump(tsne, f)\n",
    "\n",
    "    pd.np.save(tsne_vectors_filepath, tsne_vectors)\n",
    "    \n",
    "with open(tsne_filepath, 'rb') as f:\n",
    "    tsne = pickle.load(f)\n",
    "    \n",
    "tsne_vectors = pd.np.load(tsne_vectors_filepath)\n",
    "\n",
    "tsne_vectors = pd.DataFrame(tsne_vectors,\n",
    "                            index=pd.Index(tsne_input.index),\n",
    "                            columns=[u'x_coord', u'y_coord'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x_coord</th>\n",
       "      <th>y_coord</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>-PRON-</th>\n",
       "      <td>-4.023690</td>\n",
       "      <td>-14.034254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>good</th>\n",
       "      <td>-0.155021</td>\n",
       "      <td>-27.410044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>food</th>\n",
       "      <td>18.706568</td>\n",
       "      <td>35.679836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>order</th>\n",
       "      <td>4.488968</td>\n",
       "      <td>-15.051637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>great</th>\n",
       "      <td>1.070059</td>\n",
       "      <td>-28.578657</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          x_coord    y_coord\n",
       "-PRON-  -4.023690 -14.034254\n",
       "good    -0.155021 -27.410044\n",
       "food    18.706568  35.679836\n",
       "order    4.488968 -15.051637\n",
       "great    1.070059 -28.578657"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tsne_vectors.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "tsne_vectors[u'word'] = tsne_vectors.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 1\n",
    "%aimport bokeh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "%reload_ext autoreload"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div class=\"bk-root\">\n",
       "        <a href=\"https://bokeh.pydata.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
       "        <span id=\"58dc6826-9535-4c5b-acf7-229790dd6ffa\">Loading BokehJS ...</span>\n",
       "    </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "(function(root) {\n",
       "  function now() {\n",
       "    return new Date();\n",
       "  }\n",
       "\n",
       "  var force = true;\n",
       "\n",
       "  if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n",
       "    root._bokeh_onload_callbacks = [];\n",
       "    root._bokeh_is_loading = undefined;\n",
       "  }\n",
       "\n",
       "  var JS_MIME_TYPE = 'application/javascript';\n",
       "  var HTML_MIME_TYPE = 'text/html';\n",
       "  var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
       "  var CLASS_NAME = 'output_bokeh rendered_html';\n",
       "\n",
       "  /**\n",
       "   * Render data to the DOM node\n",
       "   */\n",
       "  function render(props, node) {\n",
       "    var script = document.createElement(\"script\");\n",
       "    node.appendChild(script);\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when an output is cleared or removed\n",
       "   */\n",
       "  function handleClearOutput(event, handle) {\n",
       "    var cell = handle.cell;\n",
       "\n",
       "    var id = cell.output_area._bokeh_element_id;\n",
       "    var server_id = cell.output_area._bokeh_server_id;\n",
       "    // Clean up Bokeh references\n",
       "    if (id !== undefined) {\n",
       "      Bokeh.index[id].model.document.clear();\n",
       "      delete Bokeh.index[id];\n",
       "    }\n",
       "\n",
       "    if (server_id !== undefined) {\n",
       "      // Clean up Bokeh references\n",
       "      var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
       "      cell.notebook.kernel.execute(cmd, {\n",
       "        iopub: {\n",
       "          output: function(msg) {\n",
       "            var element_id = msg.content.text.trim();\n",
       "            Bokeh.index[element_id].model.document.clear();\n",
       "            delete Bokeh.index[element_id];\n",
       "          }\n",
       "        }\n",
       "      });\n",
       "      // Destroy server and session\n",
       "      var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
       "      cell.notebook.kernel.execute(cmd);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when a new output is added\n",
       "   */\n",
       "  function handleAddOutput(event, handle) {\n",
       "    var output_area = handle.output_area;\n",
       "    var output = handle.output;\n",
       "\n",
       "    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n",
       "    if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n",
       "      return\n",
       "    }\n",
       "\n",
       "    var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
       "\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
       "      toinsert[0].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
       "      // store reference to embed id on output_area\n",
       "      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
       "    }\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
       "      var bk_div = document.createElement(\"div\");\n",
       "      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
       "      var script_attrs = bk_div.children[0].attributes;\n",
       "      for (var i = 0; i < script_attrs.length; i++) {\n",
       "        toinsert[0].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
       "      }\n",
       "      // store reference to server id on output_area\n",
       "      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
       "    }\n",
       "  }\n",
       "\n",
       "  function register_renderer(events, OutputArea) {\n",
       "\n",
       "    function append_mime(data, metadata, element) {\n",
       "      // create a DOM node to render to\n",
       "      var toinsert = this.create_output_subarea(\n",
       "        metadata,\n",
       "        CLASS_NAME,\n",
       "        EXEC_MIME_TYPE\n",
       "      );\n",
       "      this.keyboard_manager.register_events(toinsert);\n",
       "      // Render to node\n",
       "      var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
       "      render(props, toinsert[0]);\n",
       "      element.append(toinsert);\n",
       "      return toinsert\n",
       "    }\n",
       "\n",
       "    /* Handle when an output is cleared or removed */\n",
       "    events.on('clear_output.CodeCell', handleClearOutput);\n",
       "    events.on('delete.Cell', handleClearOutput);\n",
       "\n",
       "    /* Handle when a new output is added */\n",
       "    events.on('output_added.OutputArea', handleAddOutput);\n",
       "\n",
       "    /**\n",
       "     * Register the mime type and append_mime function with output_area\n",
       "     */\n",
       "    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
       "      /* Is output safe? */\n",
       "      safe: true,\n",
       "      /* Index of renderer in `output_area.display_order` */\n",
       "      index: 0\n",
       "    });\n",
       "  }\n",
       "\n",
       "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
       "  if (root.Jupyter !== undefined) {\n",
       "    var events = require('base/js/events');\n",
       "    var OutputArea = require('notebook/js/outputarea').OutputArea;\n",
       "\n",
       "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
       "      register_renderer(events, OutputArea);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  \n",
       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "    root._bokeh_timeout = Date.now() + 5000;\n",
       "    root._bokeh_failed_load = false;\n",
       "  }\n",
       "\n",
       "  var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "     \"<div style='background-color: #fdd'>\\n\"+\n",
       "     \"<p>\\n\"+\n",
       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "     \"</p>\\n\"+\n",
       "     \"<ul>\\n\"+\n",
       "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "     \"</ul>\\n\"+\n",
       "     \"<code>\\n\"+\n",
       "     \"from bokeh.resources import INLINE\\n\"+\n",
       "     \"output_notebook(resources=INLINE)\\n\"+\n",
       "     \"</code>\\n\"+\n",
       "     \"</div>\"}};\n",
       "\n",
       "  function display_loaded() {\n",
       "    var el = document.getElementById(\"58dc6826-9535-4c5b-acf7-229790dd6ffa\");\n",
       "    if (el != null) {\n",
       "      el.textContent = \"BokehJS is loading...\";\n",
       "    }\n",
       "    if (root.Bokeh !== undefined) {\n",
       "      if (el != null) {\n",
       "        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
       "      }\n",
       "    } else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(display_loaded, 100)\n",
       "    }\n",
       "  }\n",
       "\n",
       "\n",
       "  function run_callbacks() {\n",
       "    try {\n",
       "      root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
       "    }\n",
       "    finally {\n",
       "      delete root._bokeh_onload_callbacks\n",
       "    }\n",
       "    console.info(\"Bokeh: all callbacks have finished\");\n",
       "  }\n",
       "\n",
       "  function load_libs(js_urls, callback) {\n",
       "    root._bokeh_onload_callbacks.push(callback);\n",
       "    if (root._bokeh_is_loading > 0) {\n",
       "      console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "      return null;\n",
       "    }\n",
       "    if (js_urls == null || js_urls.length === 0) {\n",
       "      run_callbacks();\n",
       "      return null;\n",
       "    }\n",
       "    console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    root._bokeh_is_loading = js_urls.length;\n",
       "    for (var i = 0; i < js_urls.length; i++) {\n",
       "      var url = js_urls[i];\n",
       "      var s = document.createElement('script');\n",
       "      s.src = url;\n",
       "      s.async = false;\n",
       "      s.onreadystatechange = s.onload = function() {\n",
       "        root._bokeh_is_loading--;\n",
       "        if (root._bokeh_is_loading === 0) {\n",
       "          console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
       "          run_callbacks()\n",
       "        }\n",
       "      };\n",
       "      s.onerror = function() {\n",
       "        console.warn(\"failed to load library \" + url);\n",
       "      };\n",
       "      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "    }\n",
       "  };var element = document.getElementById(\"58dc6826-9535-4c5b-acf7-229790dd6ffa\");\n",
       "  if (element == null) {\n",
       "    console.log(\"Bokeh: ERROR: autoload.js configured with elementid '58dc6826-9535-4c5b-acf7-229790dd6ffa' but no matching script tag was found. \")\n",
       "    return false;\n",
       "  }\n",
       "\n",
       "  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.14.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.14.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.14.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-0.12.14.min.js\"];\n",
       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    \n",
       "    function(Bokeh) {\n",
       "      \n",
       "    },\n",
       "    function(Bokeh) {\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.14.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.14.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.14.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.14.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.14.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.14.min.css\");\n",
       "    }\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    \n",
       "    if ((root.Bokeh !== undefined) || (force === true)) {\n",
       "      for (var i = 0; i < inline_js.length; i++) {\n",
       "        inline_js[i].call(root, root.Bokeh);\n",
       "      }if (force === true) {\n",
       "        display_loaded();\n",
       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!root._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      root._bokeh_failed_load = true;\n",
       "    } else if (force !== true) {\n",
       "      var cell = $(document.getElementById(\"58dc6826-9535-4c5b-acf7-229790dd6ffa\")).parents('.cell').data().cell;\n",
       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "    }\n",
       "\n",
       "  }\n",
       "\n",
       "  if (root._bokeh_is_loading === 0) {\n",
       "    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(js_urls, function() {\n",
       "      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(window));"
      ],
      "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n\n  if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n  \n\n  \n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  var NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    var el = document.getElementById(\"58dc6826-9535-4c5b-acf7-229790dd6ffa\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n    }\n    finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.info(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(js_urls, callback) {\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = js_urls.length;\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      var s = document.createElement('script');\n      s.src = url;\n      s.async = false;\n      s.onreadystatechange = s.onload = function() {\n        root._bokeh_is_loading--;\n        if (root._bokeh_is_loading === 0) {\n          console.log(\"Bokeh: all BokehJS libraries loaded\");\n          run_callbacks()\n        }\n      };\n      s.onerror = function() {\n        console.warn(\"failed to load library \" + url);\n      };\n      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.getElementsByTagName(\"head\")[0].appendChild(s);\n    }\n  };var element = document.getElementById(\"58dc6826-9535-4c5b-acf7-229790dd6ffa\");\n  if (element == null) {\n    console.log(\"Bokeh: ERROR: autoload.js configured with elementid '58dc6826-9535-4c5b-acf7-229790dd6ffa' but no matching script tag was found. \")\n    return false;\n  }\n\n  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.14.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.14.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.14.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-0.12.14.min.js\"];\n\n  var inline_js = [\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    \n    function(Bokeh) {\n      \n    },\n    function(Bokeh) {\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.14.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.14.min.css\");\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.14.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.14.min.css\");\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.14.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.14.min.css\");\n    }\n  ];\n\n  function run_inline_js() {\n    \n    if ((root.Bokeh !== undefined) || (force === true)) {\n      for (var i = 0; i < inline_js.length; i++) {\n        inline_js[i].call(root, root.Bokeh);\n      }if (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      var cell = $(document.getElementById(\"58dc6826-9535-4c5b-acf7-229790dd6ffa\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(js_urls, function() {\n      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from bokeh.plotting import figure, show, output_notebook\n",
    "from bokeh.models import HoverTool, ColumnDataSource, value\n",
    "\n",
    "output_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<div class=\"bk-root\">\n",
       "    <div class=\"bk-plotdiv\" id=\"917e6403-dbda-4cbe-9308-e849b7ea43d9\"></div>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "    \n",
       "  var docs_json = 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\",\"dtype\":\"float32\",\"shape\":[3413]},\"y_coord\":{\"__ndarray__\":\"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\",\"dtype\":\"float32\",\"shape\":[3413]}}},\"id\":\"173dfa68-237b-4fbd-9b63-39028830eb4d\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.1},\"fill_color\":{\"value\":\"blue\"},\"line_alpha\":{\"value\":0.2},\"line_color\":{\"value\":\"blue\"},\"size\":{\"units\":\"screen\",\"value\":10},\"x\":{\"field\":\"x_coord\"},\"y\":{\"field\":\"y_coord\"}},\"id\":\"bd8a6f96-413c-4dd3-b34b-66ce4cd27445\",\"type\":\"Circle\"},{\"attributes\":{\"data_source\":{\"id\":\"173dfa68-237b-4fbd-9b63-39028830eb4d\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"bd8a6f96-413c-4dd3-b34b-66ce4cd27445\",\"type\":\"Circle\"},\"hover_glyph\":{\"id\":\"53d992dc-c665-446c-9161-069371975883\",\"type\":\"Circle\"},\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"08c27d48-82b7-4793-af7a-2642d682bc1e\",\"type\":\"Circle\"},\"selection_glyph\":null,\"view\":{\"id\":\"ecae9c15-fd5e-40e6-bceb-a14c382b9209\",\"type\":\"CDSView\"}},\"id\":\"306e761a-d45f-4b16-9ca6-032eeb18a48f\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"plot\":null,\"text\":\"t-SNE Word Embeddings\",\"text_font_size\":{\"value\":\"16pt\"}},\"id\":\"e7f7040d-1e28-4305-9efe-b2705b1bb60f\",\"type\":\"Title\"},{\"attributes\":{\"callback\":null,\"tooltips\":\"@word\"},\"id\":\"64d73252-e7e9-46f6-b7fd-eca9456d75b5\",\"type\":\"HoverTool\"},{\"attributes\":{\"active_drag\":\"auto\",\"active_inspect\":\"auto\",\"active_scroll\":{\"id\":\"3a659d92-a007-4ba0-97a4-ee58d28205bc\",\"type\":\"WheelZoomTool\"},\"active_tap\":\"auto\",\"tools\":[{\"id\":\"77b7c1ad-b208-4f67-bd36-c46c9a8ac424\",\"type\":\"PanTool\"},{\"id\":\"3a659d92-a007-4ba0-97a4-ee58d28205bc\",\"type\":\"WheelZoomTool\"},{\"id\":\"eb486231-286d-45e7-86c1-262723e81648\",\"type\":\"BoxZoomTool\"},{\"id\":\"ab0c9619-1be5-4289-897d-aff8921783fb\",\"type\":\"BoxSelectTool\"},{\"id\":\"aec81664-2b5f-4689-9bc9-d1d3bb5486bc\",\"type\":\"ResetTool\"},{\"id\":\"64d73252-e7e9-46f6-b7fd-eca9456d75b5\",\"type\":\"HoverTool\"}]},\"id\":\"c2765dc7-79ca-4132-ad4b-d72899a70cde\",\"type\":\"Toolbar\"},{\"attributes\":{\"callback\":null},\"id\":\"ce0a5d07-605f-46a5-b7a9-90e21addebda\",\"type\":\"DataRange1d\"},{\"attributes\":{},\"id\":\"67b4c2af-2097-4009-b9cf-112ea1b7669c\",\"type\":\"LinearScale\"},{\"attributes\":{\"callback\":null},\"id\":\"00cbb66f-3cc6-4445-88df-83971cdcbd88\",\"type\":\"DataRange1d\"},{\"attributes\":{},\"id\":\"9662594b-c141-4b56-96fa-325d0e8d7df8\",\"type\":\"LinearScale\"},{\"attributes\":{\"fill_color\":{\"value\":\"#1f77b4\"},\"size\":{\"units\":\"screen\",\"value\":10},\"x\":{\"field\":\"x_coord\"},\"y\":{\"field\":\"y_coord\"}},\"id\":\"53d992dc-c665-446c-9161-069371975883\",\"type\":\"Circle\"},{\"attributes\":{\"formatter\":{\"id\":\"64f7f6ee-3cee-4f7c-91dc-753b60fd5b57\",\"type\":\"BasicTickFormatter\"},\"plot\":{\"id\":\"5cce556f-323a-415e-b22d-164c463fb9ca\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"badd5dec-aa9f-463a-b1ab-4b7364d6d8bb\",\"type\":\"BasicTicker\"},\"visible\":false},\"id\":\"568802f0-56ee-4710-be87-cb454674a029\",\"type\":\"LinearAxis\"},{\"attributes\":{},\"id\":\"badd5dec-aa9f-463a-b1ab-4b7364d6d8bb\",\"type\":\"BasicTicker\"},{\"attributes\":{\"grid_line_color\":{\"value\":null},\"plot\":{\"id\":\"5cce556f-323a-415e-b22d-164c463fb9ca\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"badd5dec-aa9f-463a-b1ab-4b7364d6d8bb\",\"type\":\"BasicTicker\"}},\"id\":\"31a6ca0b-f279-43da-9e1b-963ce7ee138a\",\"type\":\"Grid\"},{\"attributes\":{\"formatter\":{\"id\":\"339777ec-6be8-42c5-9c86-c011a4ab4e82\",\"type\":\"BasicTickFormatter\"},\"plot\":{\"id\":\"5cce556f-323a-415e-b22d-164c463fb9ca\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"b911b8a4-6b62-43b0-af29-2911bf161f08\",\"type\":\"BasicTicker\"},\"visible\":false},\"id\":\"abdf3964-72a7-44fd-9041-ad4509595cee\",\"type\":\"LinearAxis\"},{\"attributes\":{},\"id\":\"b911b8a4-6b62-43b0-af29-2911bf161f08\",\"type\":\"BasicTicker\"},{\"attributes\":{\"dimension\":1,\"grid_line_color\":{\"value\":null},\"plot\":{\"id\":\"5cce556f-323a-415e-b22d-164c463fb9ca\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"b911b8a4-6b62-43b0-af29-2911bf161f08\",\"type\":\"BasicTicker\"}},\"id\":\"809c1ad2-9e8c-45ab-89bf-ddb2a8226ff6\",\"type\":\"Grid\"},{\"attributes\":{\"callback\":null,\"overlay\":{\"id\":\"87de4d50-ea76-4aed-98c3-b16441234b12\",\"type\":\"BoxAnnotation\"},\"renderers\":[{\"id\":\"306e761a-d45f-4b16-9ca6-032eeb18a48f\",\"type\":\"GlyphRenderer\"}]},\"id\":\"ab0c9619-1be5-4289-897d-aff8921783fb\",\"type\":\"BoxSelectTool\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.1},\"fill_color\":{\"value\":\"#1f77b4\"},\"line_alpha\":{\"value\":0.1},\"line_color\":{\"value\":\"#1f77b4\"},\"size\":{\"units\":\"screen\",\"value\":10},\"x\":{\"field\":\"x_coord\"},\"y\":{\"field\":\"y_coord\"}},\"id\":\"08c27d48-82b7-4793-af7a-2642d682bc1e\",\"type\":\"Circle\"},{\"attributes\":{\"source\":{\"id\":\"173dfa68-237b-4fbd-9b63-39028830eb4d\",\"type\":\"ColumnDataSource\"}},\"id\":\"ecae9c15-fd5e-40e6-bceb-a14c382b9209\",\"type\":\"CDSView\"},{\"attributes\":{},\"id\":\"77b7c1ad-b208-4f67-bd36-c46c9a8ac424\",\"type\":\"PanTool\"},{\"attributes\":{},\"id\":\"3a659d92-a007-4ba0-97a4-ee58d28205bc\",\"type\":\"WheelZoomTool\"},{\"attributes\":{\"overlay\":{\"id\":\"da37d172-e56d-47a8-9cae-f54cd10b7da6\",\"type\":\"BoxAnnotation\"}},\"id\":\"eb486231-286d-45e7-86c1-262723e81648\",\"type\":\"BoxZoomTool\"}],\"root_ids\":[\"5cce556f-323a-415e-b22d-164c463fb9ca\"]},\"title\":\"Bokeh Application\",\"version\":\"0.12.14\"}};\n",
       "  var render_items = [{\"docid\":\"c7da71af-dc4b-4a24-b656-f66ef3dabad2\",\"elementid\":\"917e6403-dbda-4cbe-9308-e849b7ea43d9\",\"modelid\":\"5cce556f-323a-415e-b22d-164c463fb9ca\"}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        embed_document(root);\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "      attempts++;\n",
       "      if (attempts > 100) {\n",
       "        console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\")\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "5cce556f-323a-415e-b22d-164c463fb9ca"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# add our DataFrame as a ColumnDataSource for Bokeh\n",
    "plot_data = ColumnDataSource(tsne_vectors)\n",
    "\n",
    "# create the plot and configure the\n",
    "# title, dimensions, and tools\n",
    "tsne_plot = figure(title=u't-SNE Word Embeddings',\n",
    "                   plot_width = 800,\n",
    "                   plot_height = 800,\n",
    "                   tools= (u'pan, wheel_zoom, box_zoom,'\n",
    "                           u'box_select, reset'),\n",
    "                   active_scroll=u'wheel_zoom')\n",
    "\n",
    "# add a hover tool to display words on roll-over\n",
    "tsne_plot.add_tools( HoverTool(tooltips = u'@word') )\n",
    "\n",
    "# draw the words as circles on the plot\n",
    "tsne_plot.circle(u'x_coord', u'y_coord', source=plot_data,\n",
    "                 color=u'blue', line_alpha=0.2, fill_alpha=0.1,\n",
    "                 size=10, hover_line_color=u'black')\n",
    "\n",
    "# configure visual elements of the plot\n",
    "tsne_plot.title.text_font_size = value(u'16pt')\n",
    "tsne_plot.xaxis.visible = False\n",
    "tsne_plot.yaxis.visible = False\n",
    "tsne_plot.grid.grid_line_color = None\n",
    "tsne_plot.outline_line_color = None\n",
    "\n",
    "# engage!\n",
    "show(tsne_plot);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "from textblob      import TextBlob\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "456 4 42\n"
     ]
    }
   ],
   "source": [
    "sentiments = {'positive': [],\n",
    "             'neutral': [],\n",
    "              'negative': []}\n",
    "# sentiments = Counter()\n",
    "with open(review_txt_filepath) as rf:\n",
    "    positive = 0\n",
    "    neutral  = 0\n",
    "    negative = 0\n",
    "    for i, sent in enumerate(rf):\n",
    "        analysis  = TextBlob(sent)\n",
    "        sentiment = analysis.sentiment.polarity\n",
    "\n",
    "        if sentiment > 0:\n",
    "            positive += 1\n",
    "            sentiments['positive'].append(sent)\n",
    "        elif sentiment == 0:\n",
    "            neutral += 1\n",
    "            sentiments['neutral'].append(sent)\n",
    "\n",
    "        else:\n",
    "            negative += 1\n",
    "            sentiments['negative'].append(sent)\n",
    "\n",
    "        if i > 500:\n",
    "            break\n",
    "\n",
    "print(positive, neutral, negative)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['burgers are very big portions here.\\\\n\\\\ndefinitely order the onion ring tower to share...\\\\n\\\\nMilkshakes are tasty! My personal favourite - the vanilla one.\\n',\n",
       " \"Ate here with my girlfriend this evening. She had the Mongolian entree and I had the Pad Cha Cha Cha entree. Both were excellent.\\\\n\\\\nWe've also eaten here once before and had no complaints that time either.\\\\n\\\\nService was prompt and friendly.\\\\n\\\\nWe'll definitely be back again.\\n\",\n",
       " 'I have only three words for this place. Yum. Yum. and Yum.\\n',\n",
       " 'I have since been back twice, once for breakfast and another time for lunch. Stan, thank you for sacking whoever was dicking off the thing last time I came in to visit.\\\\n\\\\n\"That\\'ll do, Pig. That\\'ll do.\" Seal of Approval.\\n']"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentiments['neutral']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Server was a little rude.\\\\n\\\\nOrdered the calamari, duck confit poutine and the trout fish with miso soba - all very tasty. Definitely not your typical diner.\\n',\n",
       " 'Wanted to check out this place due to all the hype I had heard. My friend wanted to come here for her birthday. We had a group of seven and based on the way the seating works (as it is communal), we wind up sharing with another group of five. \\\\n\\\\nFood was ok - not sure what the hype was. Almost 75% of the dishes had some sort of cheese in it.\\\\n\\\\nThis place is very loud so not ideal for catching up/talking. Not a great place to go if you have any gluten or vegan restrictions.\\\\n\\\\nInteresting concept. Would definitely recommend to try this place once - but definitely not worth the 30- 45 mins wait.\\n',\n",
       " \"really excited to hear of this restaurant coming to Toronto. When it finally opened, my friend and I were really excited to try this place.\\\\n\\\\nService here is not great, it felt like they had forgotten about us and that they didn't care about the service as everything came out one at a time. My friend and I had each ordered a ginger beer, which came out at two separate times (it was almost as if they had to go in the back and make it themselves - that's how long it took).\\\\n\\\\nThere is absolutely no vegetarian options, except for the one cold noodle - the mushroom bun is not vegetarian.\\\\n\\\\nThe pork belly bun was nothing special - just a piece of meat with the bun. For my vegetarian friend, we had ordered the bun plain so she could experience it as well, and they literally just gave it to us plain, with no sauce, no kimchi.\\\\n\\\\nThe Momofuko ramen was awful. It was bland and mushy. After a few bites, I couldn't handle eating anymore.\\\\n\\\\nMomofuko is not the place to go if you want real ramen. I've had better experience with instant noodles.\\n\",\n",
       " 'Food is very bland - not authentic at all.\\\\n\\\\nmeant to cater to the customers who have never eaten Vietnamese food before.\\\\n\\\\nDefinitely will not be returning!\\n',\n",
       " 'While looking at different restaurants in the area, came upon this restaurant posting and decided to try it, as it is down the street from work.\\\\n\\\\nNot bad when you want something slightly cheaper than Congee Star. It is a mix of chinese and choy suey menu.\\n',\n",
       " 'Service is really slow here. I waited for over 25 mins just for takeout. Definitely not worth the wait. However, they seem to have live music Friday nites, so if you are looking for entertainment and food sit-in is probably a lot faster\\n',\n",
       " \"If you have not yet tried Wasabi - don't bother.\\\\n\\\\nIt's expensive and the food is disgusting. You have to order the sushi at the table, but everything else (i.e. salads, soups, cooked food) you can get up and go get it yourself.\\\\n\\\\nThe food is very bland, the sushi does not taste like anything - not even sure if the fish is fresh.\\\\n\\\\nOn the weekends, everyone seems to be there for the snow crab legs - if that's what you are coming for, forget it! You have to fight/wait in-line to get it. It's probably easier to eat it at Mandarin.\\\\n\\\\nIf anyone suggests to go there, I highly recommend you go somewhere else!\\n\",\n",
       " \"worse customer service ever. \\\\nManager on duty was rude. She didn't care that I had negative feelings about this place when I said that I would never come back again!\\\\nRestaurant has gone downhill since they renovated!!\\n\",\n",
       " \"Came here after dinner to meet up with some friends...it was nice and roomy for 7 people. \\\\n\\\\nWe ordered a plate of Rosemary fries to share, which wasn't bad, however when served it was room temperature and did not taste like it just came out of the fryer.\\\\n\\\\nBF had ordered a couple of beers, which he was not impressed at all with.\\n\",\n",
       " \"after reading reviews on this place, thought I would take my family here to give it a try.\\\\n\\\\nbut after driving in circles a few times, we realized that this place had closed down - couldn't find it at all!\\n\"]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentiments['negative'][:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from spacy import displacy\n",
    "import spacy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "nlp = spacy.load('en')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'sentiments' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-5-a4d975ef58bd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msentiments\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'negative'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m9\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreplace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"\\\\n\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\" . \"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mdoc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnlp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mdoc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'sentiments' is not defined"
     ]
    }
   ],
   "source": [
    "s = sentiments['negative'][9].replace(\"\\\\n\",\" . \")\n",
    "s\n",
    "doc = nlp(s)\n",
    "doc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = \"'Wanted to check out this place due to all the hype I had heard. My friend wanted to come here for her birthday. We had a group of seven and based on the way the seating works (as it is communal), we wind up sharing with another group of five. \\\\n\\\\nFood was ok - not sure what the hype was. Almost 75% of the dishes had some sort of cheese in it.\\\\n\\\\nThis place is very loud so not ideal for catching up/talking. Not a great place to go if you have any gluten or vegan restrictions.\\\\n\\\\nInteresting concept. Would definitely recommend to try this place once - but definitely not worth the 30- 45 mins wait.\\n',\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'Wanted to check out this place due to all the hype I had heard. My friend wanted to come here for her birthday. We had a group of seven and based on the way the seating works (as it is communal), we wind up sharing with another group of five. \\n\\nFood was ok - not sure what the hype was. Almost 75% of the dishes had some sort of cheese in it.\\n\\nThis place is very loud so not ideal for catching up/talking. Not a great place to go if you have any gluten or vegan restrictions.\\n\\nInteresting concept. Would definitely recommend to try this place once - but definitely not worth the 30- 45 mins wait.\n",
      "',\n"
     ]
    }
   ],
   "source": [
    "print(nlp(s))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" id=\"0\" class=\"displacy\" width=\"20350\" height=\"1012.0\" style=\"max-width: none; height: 1012.0px; color: #000000; background: #ffffff; font-family: Arial\">\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"50\">'</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"50\">PUNCT</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"225\">Wanted</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"225\">VERB</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"400\">to</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"400\">PART</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"575\">check</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"575\">VERB</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"750\">out</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"750\">PART</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"925\">this</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"925\">DET</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"1100\">place</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"1100\">NOUN</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"1275\">due</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"1275\">ADP</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"1450\">to</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"1450\">ADP</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"1625\">all</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"1625\">ADJ</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"1800\">the</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"1800\">DET</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"1975\">hype</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"1975\">NOUN</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"2150\">I</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"2150\">PRON</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"2325\">had</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"2325\">VERB</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"2500\">heard.</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"2500\">VERB</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"2675\">My</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"2675\">ADJ</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"2850\">friend</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"2850\">NOUN</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"3025\">wanted</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"3025\">VERB</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"3200\">to</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"3200\">PART</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"3375\">come</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"3375\">VERB</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"3550\">here</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"3550\">ADV</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"3725\">for</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"3725\">ADP</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"3900\">her</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"3900\">ADJ</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"4075\">birthday.</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"4075\">NOUN</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"4250\">We</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"4250\">PRON</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"4425\">had</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"4425\">VERB</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"4600\">a</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"4600\">DET</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"4775\">group</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"4775\">NOUN</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"4950\">of</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"4950\">ADP</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"5125\">seven</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"5125\">NUM</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"5300\">and</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"5300\">CCONJ</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"5475\">based</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"5475\">VERB</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"5650\">on</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"5650\">ADP</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"5825\">the</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"5825\">DET</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"6000\">way</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"6000\">NOUN</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"6175\">the</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"6175\">DET</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"6350\">seating</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"6350\">NOUN</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"6525\">works (</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"6525\">NOUN</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"6700\">as</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"6700\">ADP</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"6875\">it</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"6875\">PRON</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"7050\">is</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"7050\">VERB</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"7225\">communal),</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"7225\">ADJ</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"7400\">we</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"7400\">PRON</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"7575\">wind</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"7575\">VERB</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"7750\">up</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"7750\">PART</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"7925\">sharing</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"7925\">VERB</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"8100\">with</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"8100\">ADP</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"8275\">another</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"8275\">DET</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"8450\">group</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"8450\">NOUN</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"8625\">of</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"8625\">ADP</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"8800\">five.</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"8800\">NUM</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"8975\">\\n\\nFood</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"8975\">ADV</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"9150\">was</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"9150\">VERB</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"9325\">ok -</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"9325\">ADJ</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"9500\">not</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"9500\">ADV</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"9675\">sure</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"9675\">ADJ</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"9850\">what</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"9850\">NOUN</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"10025\">the</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"10025\">DET</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"10200\">hype</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"10200\">NOUN</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"10375\">was.</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"10375\">VERB</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"10550\">Almost</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"10550\">ADV</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"10725\">75%</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"10725\">NUM</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"10900\">of</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"10900\">ADP</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"11075\">the</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"11075\">DET</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"11250\">dishes</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"11250\">NOUN</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"11425\">had</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"11425\">VERB</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"11600\">some</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"11600\">DET</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"11775\">sort</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"11775\">NOUN</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"11950\">of</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"11950\">ADP</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"12125\">cheese</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"12125\">NOUN</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"12300\">in</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"12300\">ADP</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"12475\">it.\\n\\nThis</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"12475\">ADJ</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"12650\">place</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"12650\">NOUN</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"12825\">is</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"12825\">VERB</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"13000\">very</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"13000\">ADV</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"13175\">loud</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"13175\">ADJ</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"13350\">so</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"13350\">ADV</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"13525\">not</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"13525\">ADV</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"13700\">ideal</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"13700\">ADJ</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"13875\">for</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"13875\">ADP</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"14050\">catching</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"14050\">VERB</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"14225\">up/</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"14225\">PART</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"14400\">talking.</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"14400\">VERB</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"14575\">Not</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"14575\">ADV</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"14750\">a</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"14750\">DET</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"14925\">great</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"14925\">ADJ</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"15100\">place</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"15100\">NOUN</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"15275\">to</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"15275\">PART</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"15450\">go</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"15450\">VERB</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"15625\">if</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"15625\">ADP</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"15800\">you</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"15800\">PRON</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"15975\">have</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"15975\">VERB</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"16150\">any</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"16150\">DET</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"16325\">gluten</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"16325\">ADJ</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"16500\">or</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"16500\">CCONJ</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"16675\">vegan</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"16675\">NOUN</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"16850\">restrictions.\\n\\nInteresting</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"16850\">VERB</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"17025\">concept.</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"17025\">NOUN</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"17200\">Would</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"17200\">VERB</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"17375\">definitely</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"17375\">ADV</tspan>\n",
      "</text>\n",
      "\n",
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      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"17550\">VERB</tspan>\n",
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      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"17725\">to</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"17725\">PART</tspan>\n",
      "</text>\n",
      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"17900\">try</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"17900\">VERB</tspan>\n",
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      "\n",
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      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"18075\">DET</tspan>\n",
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      "\n",
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      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"18250\">place</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"18250\">NOUN</tspan>\n",
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      "\n",
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      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"18600\">but</tspan>\n",
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      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
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      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"18775\">ADV</tspan>\n",
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      "\n",
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      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"18950\">ADV</tspan>\n",
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      "\n",
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      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"19125\">worth</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"19125\">ADJ</tspan>\n",
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      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"19300\">the</tspan>\n",
      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"19300\">DET</tspan>\n",
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      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
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      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"19475\">NUM</tspan>\n",
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      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
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      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"19650\">NUM</tspan>\n",
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      "\n",
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      "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"19825\">NOUN</tspan>\n",
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      "\n",
      "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"922.0\">\n",
      "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"20000\">wait.</tspan>\n",
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      "\n",
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      "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
      "        <textPath xlink:href=\"#arrow-0-85\" class=\"displacy-label\" startOffset=\"50%\" fill=\"currentColor\" text-anchor=\"middle\">advcl</textPath>\n",
      "    </text>\n",
      "    <path class=\"displacy-arrowhead\" d=\"M15940.0,879.0 L15948.0,867.0 15932.0,867.0\" fill=\"currentColor\"/>\n",
      "</g>\n",
      "\n",
      "<g class=\"displacy-arrow\">\n",
      "    <path class=\"displacy-arc\" id=\"arrow-0-86\" stroke-width=\"2px\" d=\"M16170,877.0 C16170,789.5 16280.0,789.5 16280.0,877.0\" fill=\"none\" stroke=\"currentColor\"/>\n",
      "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
      "        <textPath xlink:href=\"#arrow-0-86\" class=\"displacy-label\" startOffset=\"50%\" fill=\"currentColor\" text-anchor=\"middle\">det</textPath>\n",
      "    </text>\n",
      "    <path class=\"displacy-arrowhead\" d=\"M16170,879.0 L16162,867.0 16178,867.0\" fill=\"currentColor\"/>\n",
      "</g>\n",
      "\n",
      "<g class=\"displacy-arrow\">\n",
      "    <path class=\"displacy-arc\" id=\"arrow-0-87\" stroke-width=\"2px\" d=\"M16345,877.0 C16345,527.0 16995.0,527.0 16995.0,877.0\" fill=\"none\" stroke=\"currentColor\"/>\n",
      "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
      "        <textPath xlink:href=\"#arrow-0-87\" class=\"displacy-label\" startOffset=\"50%\" fill=\"currentColor\" text-anchor=\"middle\">nmod</textPath>\n",
      "    </text>\n",
      "    <path class=\"displacy-arrowhead\" d=\"M16345,879.0 L16337,867.0 16353,867.0\" fill=\"currentColor\"/>\n",
      "</g>\n",
      "\n",
      "<g class=\"displacy-arrow\">\n",
      "    <path class=\"displacy-arc\" id=\"arrow-0-88\" stroke-width=\"2px\" d=\"M16345,877.0 C16345,789.5 16455.0,789.5 16455.0,877.0\" fill=\"none\" stroke=\"currentColor\"/>\n",
      "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
      "        <textPath xlink:href=\"#arrow-0-88\" class=\"displacy-label\" startOffset=\"50%\" fill=\"currentColor\" text-anchor=\"middle\">cc</textPath>\n",
      "    </text>\n",
      "    <path class=\"displacy-arrowhead\" d=\"M16455.0,879.0 L16463.0,867.0 16447.0,867.0\" fill=\"currentColor\"/>\n",
      "</g>\n",
      "\n",
      "<g class=\"displacy-arrow\">\n",
      "    <path class=\"displacy-arc\" id=\"arrow-0-89\" stroke-width=\"2px\" d=\"M16345,877.0 C16345,702.0 16635.0,702.0 16635.0,877.0\" fill=\"none\" stroke=\"currentColor\"/>\n",
      "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
      "        <textPath xlink:href=\"#arrow-0-89\" class=\"displacy-label\" startOffset=\"50%\" fill=\"currentColor\" text-anchor=\"middle\">conj</textPath>\n",
      "    </text>\n",
      "    <path class=\"displacy-arrowhead\" d=\"M16635.0,879.0 L16643.0,867.0 16627.0,867.0\" fill=\"currentColor\"/>\n",
      "</g>\n",
      "\n",
      "<g class=\"displacy-arrow\">\n",
      "    <path class=\"displacy-arc\" id=\"arrow-0-90\" stroke-width=\"2px\" d=\"M16345,877.0 C16345,614.5 16815.0,614.5 16815.0,877.0\" fill=\"none\" stroke=\"currentColor\"/>\n",
      "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
      "        <textPath xlink:href=\"#arrow-0-90\" class=\"displacy-label\" startOffset=\"50%\" fill=\"currentColor\" text-anchor=\"middle\">conj</textPath>\n",
      "    </text>\n",
      "    <path class=\"displacy-arrowhead\" d=\"M16815.0,879.0 L16823.0,867.0 16807.0,867.0\" fill=\"currentColor\"/>\n",
      "</g>\n",
      "\n",
      "<g class=\"displacy-arrow\">\n",
      "    <path class=\"displacy-arc\" id=\"arrow-0-91\" stroke-width=\"2px\" d=\"M15995,877.0 C15995,352.0 17005.0,352.0 17005.0,877.0\" fill=\"none\" stroke=\"currentColor\"/>\n",
      "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
      "        <textPath xlink:href=\"#arrow-0-91\" class=\"displacy-label\" startOffset=\"50%\" fill=\"currentColor\" text-anchor=\"middle\">dobj</textPath>\n",
      "    </text>\n",
      "    <path class=\"displacy-arrowhead\" d=\"M17005.0,879.0 L17013.0,867.0 16997.0,867.0\" fill=\"currentColor\"/>\n",
      "</g>\n",
      "\n",
      "<g class=\"displacy-arrow\">\n",
      "    <path class=\"displacy-arc\" id=\"arrow-0-92\" stroke-width=\"2px\" d=\"M17220,877.0 C17220,702.0 17510.0,702.0 17510.0,877.0\" fill=\"none\" stroke=\"currentColor\"/>\n",
      "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
      "        <textPath xlink:href=\"#arrow-0-92\" class=\"displacy-label\" startOffset=\"50%\" fill=\"currentColor\" text-anchor=\"middle\">aux</textPath>\n",
      "    </text>\n",
      "    <path class=\"displacy-arrowhead\" d=\"M17220,879.0 L17212,867.0 17228,867.0\" fill=\"currentColor\"/>\n",
      "</g>\n",
      "\n",
      "<g class=\"displacy-arrow\">\n",
      "    <path class=\"displacy-arc\" id=\"arrow-0-93\" stroke-width=\"2px\" d=\"M17395,877.0 C17395,789.5 17505.0,789.5 17505.0,877.0\" fill=\"none\" stroke=\"currentColor\"/>\n",
      "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
      "        <textPath xlink:href=\"#arrow-0-93\" class=\"displacy-label\" startOffset=\"50%\" fill=\"currentColor\" text-anchor=\"middle\">advmod</textPath>\n",
      "    </text>\n",
      "    <path class=\"displacy-arrowhead\" d=\"M17395,879.0 L17387,867.0 17403,867.0\" fill=\"currentColor\"/>\n",
      "</g>\n",
      "\n",
      "<g class=\"displacy-arrow\">\n",
      "    <path class=\"displacy-arc\" id=\"arrow-0-94\" stroke-width=\"2px\" d=\"M17745,877.0 C17745,789.5 17855.0,789.5 17855.0,877.0\" fill=\"none\" stroke=\"currentColor\"/>\n",
      "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
      "        <textPath xlink:href=\"#arrow-0-94\" class=\"displacy-label\" startOffset=\"50%\" fill=\"currentColor\" text-anchor=\"middle\">aux</textPath>\n",
      "    </text>\n",
      "    <path class=\"displacy-arrowhead\" d=\"M17745,879.0 L17737,867.0 17753,867.0\" fill=\"currentColor\"/>\n",
      "</g>\n",
      "\n",
      "<g class=\"displacy-arrow\">\n",
      "    <path class=\"displacy-arc\" id=\"arrow-0-95\" stroke-width=\"2px\" d=\"M17570,877.0 C17570,702.0 17860.0,702.0 17860.0,877.0\" fill=\"none\" stroke=\"currentColor\"/>\n",
      "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
      "        <textPath xlink:href=\"#arrow-0-95\" class=\"displacy-label\" startOffset=\"50%\" fill=\"currentColor\" text-anchor=\"middle\">xcomp</textPath>\n",
      "    </text>\n",
      "    <path class=\"displacy-arrowhead\" d=\"M17860.0,879.0 L17868.0,867.0 17852.0,867.0\" fill=\"currentColor\"/>\n",
      "</g>\n",
      "\n",
      "<g class=\"displacy-arrow\">\n",
      "    <path class=\"displacy-arc\" id=\"arrow-0-96\" stroke-width=\"2px\" d=\"M18095,877.0 C18095,789.5 18205.0,789.5 18205.0,877.0\" fill=\"none\" stroke=\"currentColor\"/>\n",
      "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
      "        <textPath xlink:href=\"#arrow-0-96\" class=\"displacy-label\" startOffset=\"50%\" fill=\"currentColor\" text-anchor=\"middle\">det</textPath>\n",
      "    </text>\n",
      "    <path class=\"displacy-arrowhead\" d=\"M18095,879.0 L18087,867.0 18103,867.0\" fill=\"currentColor\"/>\n",
      "</g>\n",
      "\n",
      "<g class=\"displacy-arrow\">\n",
      "    <path class=\"displacy-arc\" id=\"arrow-0-97\" stroke-width=\"2px\" d=\"M17920,877.0 C17920,702.0 18210.0,702.0 18210.0,877.0\" fill=\"none\" stroke=\"currentColor\"/>\n",
      "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
      "        <textPath xlink:href=\"#arrow-0-97\" class=\"displacy-label\" startOffset=\"50%\" fill=\"currentColor\" text-anchor=\"middle\">dobj</textPath>\n",
      "    </text>\n",
      "    <path class=\"displacy-arrowhead\" d=\"M18210.0,879.0 L18218.0,867.0 18202.0,867.0\" fill=\"currentColor\"/>\n",
      "</g>\n",
      "\n",
      "<g class=\"displacy-arrow\">\n",
      "    <path class=\"displacy-arc\" id=\"arrow-0-98\" stroke-width=\"2px\" d=\"M17920,877.0 C17920,614.5 18390.0,614.5 18390.0,877.0\" fill=\"none\" stroke=\"currentColor\"/>\n",
      "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
      "        <textPath xlink:href=\"#arrow-0-98\" class=\"displacy-label\" startOffset=\"50%\" fill=\"currentColor\" text-anchor=\"middle\">advmod</textPath>\n",
      "    </text>\n",
      "    <path class=\"displacy-arrowhead\" d=\"M18390.0,879.0 L18398.0,867.0 18382.0,867.0\" fill=\"currentColor\"/>\n",
      "</g>\n",
      "\n",
      "<g class=\"displacy-arrow\">\n",
      "    <path class=\"displacy-arc\" id=\"arrow-0-99\" stroke-width=\"2px\" d=\"M17570,877.0 C17570,352.0 18580.0,352.0 18580.0,877.0\" fill=\"none\" stroke=\"currentColor\"/>\n",
      "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
      "        <textPath xlink:href=\"#arrow-0-99\" class=\"displacy-label\" startOffset=\"50%\" fill=\"currentColor\" text-anchor=\"middle\">cc</textPath>\n",
      "    </text>\n",
      "    <path class=\"displacy-arrowhead\" d=\"M18580.0,879.0 L18588.0,867.0 18572.0,867.0\" fill=\"currentColor\"/>\n",
      "</g>\n",
      "\n",
      "<g class=\"displacy-arrow\">\n",
      "    <path class=\"displacy-arc\" id=\"arrow-0-100\" stroke-width=\"2px\" d=\"M18795,877.0 C18795,702.0 19085.0,702.0 19085.0,877.0\" fill=\"none\" stroke=\"currentColor\"/>\n",
      "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
      "        <textPath xlink:href=\"#arrow-0-100\" class=\"displacy-label\" startOffset=\"50%\" fill=\"currentColor\" text-anchor=\"middle\">advmod</textPath>\n",
      "    </text>\n",
      "    <path class=\"displacy-arrowhead\" d=\"M18795,879.0 L18787,867.0 18803,867.0\" fill=\"currentColor\"/>\n",
      "</g>\n",
      "\n",
      "<g class=\"displacy-arrow\">\n",
      "    <path class=\"displacy-arc\" id=\"arrow-0-101\" stroke-width=\"2px\" d=\"M18970,877.0 C18970,789.5 19080.0,789.5 19080.0,877.0\" fill=\"none\" stroke=\"currentColor\"/>\n",
      "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
      "        <textPath xlink:href=\"#arrow-0-101\" class=\"displacy-label\" startOffset=\"50%\" fill=\"currentColor\" text-anchor=\"middle\">neg</textPath>\n",
      "    </text>\n",
      "    <path class=\"displacy-arrowhead\" d=\"M18970,879.0 L18962,867.0 18978,867.0\" fill=\"currentColor\"/>\n",
      "</g>\n",
      "\n",
      "<g class=\"displacy-arrow\">\n",
      "    <path class=\"displacy-arc\" id=\"arrow-0-102\" stroke-width=\"2px\" d=\"M17570,877.0 C17570,177.0 19115.0,177.0 19115.0,877.0\" fill=\"none\" stroke=\"currentColor\"/>\n",
      "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
      "        <textPath xlink:href=\"#arrow-0-102\" class=\"displacy-label\" startOffset=\"50%\" fill=\"currentColor\" text-anchor=\"middle\">conj</textPath>\n",
      "    </text>\n",
      "    <path class=\"displacy-arrowhead\" d=\"M19115.0,879.0 L19123.0,867.0 19107.0,867.0\" fill=\"currentColor\"/>\n",
      "</g>\n",
      "\n",
      "<g class=\"displacy-arrow\">\n",
      "    <path class=\"displacy-arc\" id=\"arrow-0-103\" stroke-width=\"2px\" d=\"M19320,877.0 C19320,614.5 19790.0,614.5 19790.0,877.0\" fill=\"none\" stroke=\"currentColor\"/>\n",
      "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
      "        <textPath xlink:href=\"#arrow-0-103\" class=\"displacy-label\" startOffset=\"50%\" fill=\"currentColor\" text-anchor=\"middle\">det</textPath>\n",
      "    </text>\n",
      "    <path class=\"displacy-arrowhead\" d=\"M19320,879.0 L19312,867.0 19328,867.0\" fill=\"currentColor\"/>\n",
      "</g>\n",
      "\n",
      "<g class=\"displacy-arrow\">\n",
      "    <path class=\"displacy-arc\" id=\"arrow-0-104\" stroke-width=\"2px\" d=\"M19495,877.0 C19495,789.5 19605.0,789.5 19605.0,877.0\" fill=\"none\" stroke=\"currentColor\"/>\n",
      "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
      "        <textPath xlink:href=\"#arrow-0-104\" class=\"displacy-label\" startOffset=\"50%\" fill=\"currentColor\" text-anchor=\"middle\">compound</textPath>\n",
      "    </text>\n",
      "    <path class=\"displacy-arrowhead\" d=\"M19495,879.0 L19487,867.0 19503,867.0\" fill=\"currentColor\"/>\n",
      "</g>\n",
      "\n",
      "<g class=\"displacy-arrow\">\n",
      "    <path class=\"displacy-arc\" id=\"arrow-0-105\" stroke-width=\"2px\" d=\"M19670,877.0 C19670,789.5 19780.0,789.5 19780.0,877.0\" fill=\"none\" stroke=\"currentColor\"/>\n",
      "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
      "        <textPath xlink:href=\"#arrow-0-105\" class=\"displacy-label\" startOffset=\"50%\" fill=\"currentColor\" text-anchor=\"middle\">nummod</textPath>\n",
      "    </text>\n",
      "    <path class=\"displacy-arrowhead\" d=\"M19670,879.0 L19662,867.0 19678,867.0\" fill=\"currentColor\"/>\n",
      "</g>\n",
      "\n",
      "<g class=\"displacy-arrow\">\n",
      "    <path class=\"displacy-arc\" id=\"arrow-0-106\" stroke-width=\"2px\" d=\"M19845,877.0 C19845,789.5 19955.0,789.5 19955.0,877.0\" fill=\"none\" stroke=\"currentColor\"/>\n",
      "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
      "        <textPath xlink:href=\"#arrow-0-106\" class=\"displacy-label\" startOffset=\"50%\" fill=\"currentColor\" text-anchor=\"middle\">nsubj</textPath>\n",
      "    </text>\n",
      "    <path class=\"displacy-arrowhead\" d=\"M19845,879.0 L19837,867.0 19853,867.0\" fill=\"currentColor\"/>\n",
      "</g>\n",
      "\n",
      "<g class=\"displacy-arrow\">\n",
      "    <path class=\"displacy-arc\" id=\"arrow-0-107\" stroke-width=\"2px\" d=\"M19145,877.0 C19145,439.5 19975.0,439.5 19975.0,877.0\" fill=\"none\" stroke=\"currentColor\"/>\n",
      "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
      "        <textPath xlink:href=\"#arrow-0-107\" class=\"displacy-label\" startOffset=\"50%\" fill=\"currentColor\" text-anchor=\"middle\">ccomp</textPath>\n",
      "    </text>\n",
      "    <path class=\"displacy-arrowhead\" d=\"M19975.0,879.0 L19983.0,867.0 19967.0,867.0\" fill=\"currentColor\"/>\n",
      "</g>\n",
      "\n",
      "<g class=\"displacy-arrow\">\n",
      "    <path class=\"displacy-arc\" id=\"arrow-0-108\" stroke-width=\"2px\" d=\"M20020,877.0 C20020,789.5 20130.0,789.5 20130.0,877.0\" fill=\"none\" stroke=\"currentColor\"/>\n",
      "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
      "        <textPath xlink:href=\"#arrow-0-108\" class=\"displacy-label\" startOffset=\"50%\" fill=\"currentColor\" text-anchor=\"middle\"></textPath>\n",
      "    </text>\n",
      "    <path class=\"displacy-arrowhead\" d=\"M20130.0,879.0 L20138.0,867.0 20122.0,867.0\" fill=\"currentColor\"/>\n",
      "</g>\n",
      "</svg>\n"
     ]
    }
   ],
   "source": [
    "print(displacy.render(nlp(s), style='dep'))\n",
    "# displacy.render(nlp(s), style='dep',jupyter=True, options={'distance' : 100})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'review_txt_filepath' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-17-0f9dfa2b7c41>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0manalyzer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mSentimentIntensityAnalyzer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreview_txt_filepath\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mrf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m     \u001b[0mpositive\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m     \u001b[0mneutral\u001b[0m  \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'review_txt_filepath' is not defined"
     ]
    }
   ],
   "source": [
    "sentiments_vader = {'positive': [],\n",
    "             'neutral': [],\n",
    "              'negative': []}\n",
    "# sentiments = Counter()\n",
    "analyzer = SentimentIntensityAnalyzer()\n",
    "\n",
    "with open(review_txt_filepath) as rf:\n",
    "    positive = 0\n",
    "    neutral  = 0\n",
    "    negative = 0\n",
    "    for i, sent in enumerate(rf):\n",
    "        sentiment_scores = analyzer.polarity_scores(sent)\n",
    "        sentiment = get_sentiment(sentiment_scores)\n",
    "\n",
    "        if sentiment == 'pos':\n",
    "            positive += 1\n",
    "            sentiments_vader['positive'].append(sent)\n",
    "        elif sentiment == 'neu':\n",
    "            neutral += 1\n",
    "            sentiments_vader['neutral'].append(sent)\n",
    "        else:\n",
    "            negative += 1\n",
    "            sentiments_vader['negative'].append(sent)\n",
    "\n",
    "        if i > 500:\n",
    "            break\n",
    "\n",
    "print(positive, neutral, negative)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'neu'"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_sentiment(sentiment_values):\n",
    "    res = sorted(sentiment_values.items(), key=lambda x: x[1])\n",
    "    if res[-1][0] != 'compound':\n",
    "        return res[-1][0]\n",
    "    else:\n",
    "        return res[-2][0]\n",
    "\n",
    "test_score = {'compound': 0.9551, 'neg': 0.0, 'neu': 0.645, 'pos': 0.355}\n",
    "get_sentiment(test_score)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'neu'"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# positive sentiment: compound score >= 0.5\n",
    "# neutral sentiment: (compound score > -0.5) and (compound score < 0.5)\n",
    "# negative sentiment: compound score <= -0.5\n",
    "\n",
    "def get_sentiment2(sentiment_values):\n",
    "    if sentiment_values['compound'] > 0.5:\n",
    "        return 'pos'\n",
    "    elif sentiment_values['compound'] < -0.5:\n",
    "        return 'neg'\n",
    "    else:\n",
    "        return 'neu'\n",
    "    \n",
    "\n",
    "test_score = {'compound': 0.9551, 'neg': 0.0, 'neu': 0.645, 'pos': 0.355}\n",
    "get_sentiment(test_score)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "431 46 25\n"
     ]
    }
   ],
   "source": [
    "sentiments_vader = {'positive': [],\n",
    "             'neutral': [],\n",
    "              'negative': []}\n",
    "# sentiments = Counter()\n",
    "analyzer = SentimentIntensityAnalyzer()\n",
    "\n",
    "with open(review_txt_filepath) as rf:\n",
    "    positive = 0\n",
    "    neutral  = 0\n",
    "    negative = 0\n",
    "    for i, sent in enumerate(rf):\n",
    "        sent = sent.replace('\\\\n', ' . ')\n",
    "        sentiment_scores = analyzer.polarity_scores(sent)\n",
    "        sentiment = get_sentiment2(sentiment_scores)\n",
    "\n",
    "        if sentiment == 'pos':\n",
    "            positive += 1\n",
    "            sentiments_vader['positive'].append(sent)\n",
    "        elif sentiment == 'neu':\n",
    "            neutral += 1\n",
    "            sentiments_vader['neutral'].append(sent)\n",
    "        else:\n",
    "            negative += 1\n",
    "            sentiments_vader['negative'].append(sent)\n",
    "\n",
    "        if i > 500:\n",
    "            break\n",
    "\n",
    "print(positive, neutral, negative)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "el = \"worse customer service ever. \\\\nManager on duty was rude. She didn't care that I had negative feelings about this place when I said that I would never come back again!\\\\nRestaurant has gone downhill since they renovated!!\\n\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "el.split('\\\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sentiments_vader['negative'][:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sentiments_vader['neutral'][:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'neg': 0.547, 'neu': 0.116, 'pos': 0.337, 'compound': -0.4215}\n",
      "{'neg': 0.891, 'neu': 0.109, 'pos': 0.0, 'compound': -0.8481}\n",
      "{'neg': 0.413, 'neu': 0.0, 'pos': 0.587, 'compound': 0.2023}\n"
     ]
    }
   ],
   "source": [
    "experiment = [\"I kill a good human\", \"I kill a bad human\", \"I save a good killer\"]\n",
    "for sent in experiment:\n",
    "    print(analyzer.polarity_scores(sent))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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