Natural Language Toolkit.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "c0HZyH2Qo88m"
   },
   "outputs": [],
   "source": [
    "import nltk\n",
    "# nltk.download()\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "sentence = \"At eight o'clock on Thursday morning. Arthur didn't feel very good.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['At', 'eight', \"o'clock\", 'on', 'Thursday', 'morning', '.', 'Arthur', 'did', \"n't\", 'feel', 'very', 'good', '.']\n",
      "14\n"
     ]
    }
   ],
   "source": [
    "tokens = nltk.word_tokenize(sentence)\n",
    "print(tokens)\n",
    "print(len(tokens))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\"At eight o'clock on Thursday morning.\", \"Arthur didn't feel very good.\"]\n",
      "2\n"
     ]
    }
   ],
   "source": [
    "sent_tokens = nltk.sent_tokenize(sentence)\n",
    "print(sent_tokens)\n",
    "print(len(sent_tokens))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wait\n",
      "wait\n",
      "wait\n",
      "wait\n"
     ]
    }
   ],
   "source": [
    "e_words = [\"wait\", \"waiting\", \"waited\", \"waits\"]\n",
    "ps = nltk.PorterStemmer()\n",
    "for w in e_words:\n",
    "    rootWord=ps.stem(w)\n",
    "    print(rootWord)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hello\n",
      "guru99\n",
      ",\n",
      "you\n",
      "have\n",
      "to\n",
      "build\n",
      "a\n",
      "veri\n",
      "good\n",
      "site\n",
      "and\n",
      "I\n",
      "love\n",
      "visit\n",
      "your\n",
      "site\n",
      ".\n"
     ]
    }
   ],
   "source": [
    "sentence=\"Hello Guru99, You have to build a very good site and I love visiting your site.\"\n",
    "words = nltk.word_tokenize(sentence)\n",
    "ps = nltk.PorterStemmer()\n",
    "for w in words:\n",
    "    rootWord=ps.stem(w)\n",
    "    print(rootWord)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({'guru99': 1, 'is': 1, 'one': 1, 'of': 1, 'the': 1, 'best': 1, 'sites': 1, 'to': 1, 'learn': 1, 'web': 1, 'sap': 1, 'ethical': 1, 'hacking': 1, 'and': 1, 'much': 1, 'more': 1, 'online': 1})\n"
     ]
    }
   ],
   "source": [
    "import re\n",
    "import pandas as pd\n",
    "text = \"Guru99 is one of the best sites to learn WEB, SAP, Ethical Hacking and much more online.\"\n",
    "lower_case = text.lower()\n",
    "words = nltk.word_tokenize(lower_case)\n",
    "nonPunct = re.compile('.*[A-Za-z0-9].*')  # must contain a letter or digit\n",
    "filtered = [w for w in words if nonPunct.match(w)]\n",
    "counts = nltk.Counter(filtered)\n",
    "print(counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "a = \"Guru99 is the site where you can find the best tutorials for Software Testing     Tutorial, SAP Course for Beginners. Java Tutorial for Beginners and much more. Please     visit the site guru99.com and much more.\"\n",
    "words = nltk.tokenize.word_tokenize(a)\n",
    "fd = nltk.FreqDist(words)\n",
    "fd.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'hasn', 'between', 'how', 'at', 'again', 'isn', 'yourself', 'm', 'haven', 't', 'their', 'our', \"needn't\", 'out', \"it's\", \"aren't\", 'them', 'where', 'hers', 'herself', 'the', 'i', 'by', 'own', 'ourselves', 'all', 'should', 'on', 'have', \"doesn't\", 'an', 'or', 'just', 'any', 'before', \"you'll\", 'shan', \"shan't\", 'themselves', 'some', 'being', 'during', 'there', 'your', 'they', 'will', 'now', \"mustn't\", 'up', 'its', 'her', \"that'll\", \"weren't\", \"wasn't\", 'into', 'be', 'are', 'once', 'don', 'itself', 'o', 'couldn', 'if', \"hadn't\", 'whom', 'which', 'not', \"didn't\", 'more', 'wouldn', 'these', 'for', \"she's\", 'few', 'he', 'his', 'ours', 'ain', \"hasn't\", 'did', 'off', 'who', 'hadn', 'won', 'when', 'through', 'has', 'below', 'about', 'is', 'those', 'was', 'against', 'while', \"don't\", 's', 'myself', 'above', 'll', 'theirs', 'why', 'because', \"won't\", 'me', 'does', 'from', 'further', 'y', 'such', 'can', 'then', 'down', \"couldn't\", 'am', \"you're\", 'mightn', 'we', 'were', \"should've\", 'my', 'with', 'him', 'you', 've', 'doing', 'this', \"wouldn't\", 'she', \"shouldn't\", 'had', 'what', 'weren', 'too', 'most', 'yourselves', 'very', 'ma', \"you've\", 'other', 'himself', 'do', 'didn', 'shouldn', 'aren', 'yours', 'here', 'to', 'both', 'until', 'under', 'each', 'wasn', 'been', 'mustn', 'of', 'so', 'no', 'same', 'that', 'it', 'having', 'as', \"mightn't\", 'but', 'over', 'only', 'nor', 're', 'a', 'doesn', 'than', \"haven't\", \"you'd\", 'd', 'after', 'and', 'needn', \"isn't\", 'in'}\n"
     ]
    }
   ],
   "source": [
    "stop_words=set(nltk.corpus.stopwords.words(\"english\"))\n",
    "print(stop_words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tokenized Sentence: ['hello', 'mr.', 'smith', ',', 'how', 'are', 'you', 'doing', 'today', '?', 'the', 'weather', 'is', 'great', ',', 'and', 'city', 'is', 'awesome', '.', 'the', 'sky', 'is', 'pinkish-blue', '.', 'you', 'should', \"n't\", 'eat', 'cardboard']\n",
      "Filterd Sentence: ['hello', 'mr.', 'smith', ',', 'today', '?', 'weather', 'great', ',', 'city', 'awesome', '.', 'sky', 'pinkish-blue', '.', \"n't\", 'eat', 'cardboard']\n"
     ]
    }
   ],
   "source": [
    "text = \"\"\"Hello Mr. Smith, how are you doing today? The weather is great, and city is awesome.\n",
    "The sky is pinkish-blue. You shouldn't eat cardboard\"\"\"\n",
    "sentence = text.lower()\n",
    "tokenized_sent = nltk.word_tokenize(sentence)\n",
    "filtered_sent=[]\n",
    "for w in tokenized_sent:\n",
    "    if w not in stop_words:\n",
    "        filtered_sent.append(w)\n",
    "print(\"Tokenized Sentence:\",tokenized_sent)\n",
    "print(\"Filterd Sentence:\",filtered_sent)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 266
    },
    "colab_type": "code",
    "id": "gt_6WD4drwXy",
    "outputId": "b87bc70d-b80d-48c8-cc6e-0b9bfab2e132"
   },
   "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>text</th>\n",
       "      <th>stars</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[ill, be, the]</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[wow, so]</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[their]</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[ive, never]</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[easily]</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>[employees, were]</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>[great, tanning]</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                text  stars\n",
       "0     [ill, be, the]    4.0\n",
       "1          [wow, so]    4.0\n",
       "2            [their]    5.0\n",
       "3       [ive, never]    1.0\n",
       "4           [easily]    5.0\n",
       "5  [employees, were]    2.0\n",
       "6   [great, tanning]    5.0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({\n",
    "    \"text\": [['ill', 'be', 'the'], ['wow', 'so'], ['their'],['ive', 'never'],['easily'],['employees', 'were'], ['great', 'tanning']],\n",
    "    \"stars\": [4.0, 4.0, 5.0, 1.0, 5.0, 2.0, 5.0]\n",
    "})\n",
    "df.head(n=7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 266
    },
    "colab_type": "code",
    "id": "wlo4H8bb0McQ",
    "outputId": "8e398517-03c0-4df2-d51f-a5aeb1068c1b"
   },
   "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>text</th>\n",
       "      <th>stars</th>\n",
       "      <th>art_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[ill, be, the]</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[wow, so]</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[their]</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[ive, never]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[easily]</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>[employees, were]</td>\n",
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      "text/plain": [
       "                text  stars  art_id\n",
       "0     [ill, be, the]    4.0       1\n",
       "1          [wow, so]    4.0       2\n",
       "2            [their]    5.0       3\n",
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       "5  [employees, were]    2.0       6\n",
       "6   [great, tanning]    5.0       7"
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       "        text  stars  art_id\n",
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       "4     easily    5.0       5\n",
       "5  employees    2.0       6\n",
       "5       were    2.0       6\n",
       "6      great    5.0       7\n",
       "6    tanning    5.0       7"
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     "execution_count": 14,
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       "   stars  art_id  w_be  w_easily  w_employees  w_great  w_ill  w_ive  w_never  \\\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   stars  w_be  w_easily  w_employees  w_great  w_ill  w_ive  w_never  w_so  \\\n",
       "0    1.0     0         0            0        0      0      1        1     0   \n",
       "1    2.0     0         0            1        0      0      0        0     0   \n",
       "2    4.0     1         0            0        0      1      0        0     0   \n",
       "3    4.0     0         0            0        0      0      0        0     1   \n",
       "4    5.0     0         0            0        0      0      0        0     0   \n",
       "5    5.0     0         1            0        0      0      0        0     0   \n",
       "6    5.0     0         0            0        1      0      0        0     0   \n",
       "\n",
       "   w_tanning  w_the  w_their  w_were  w_wow  \n",
       "0          0      0        0       0      0  \n",
       "1          0      0        0       1      0  \n",
       "2          0      1        0       0      0  \n",
       "3          0      0        0       0      1  \n",
       "4          0      0        1       0      0  \n",
       "5          0      0        0       0      0  \n",
       "6          1      0        0       0      0  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_groupby = df_dummies.groupby([\"stars\", \"art_id\"]).sum().reset_index()\n",
    "df_words = df_groupby.drop([\"art_id\"], axis=1)\n",
    "df_words.head(n=7)"
   ]
  }
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