capstone-eda.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Capstone"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Continuation from Scraping notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Walkthrough continued"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1 GLOBAL IMPORTS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-23T18:55:47.020248Z",
     "start_time": "2018-05-23T18:55:45.902249Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import math\n",
    "from decimal import Decimal\n",
    "import re\n",
    "import string\n",
    "import unicodedata as unicode\n",
    "import nltk\n",
    "import contractions\n",
    "import inflect\n",
    "from nltk import word_tokenize, sent_tokenize\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem import LancasterStemmer, WordNetLemmatizer\n",
    "\n",
    "%config InlineBackend.figure_format = 'retina'\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2 EDA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What are your variables of interest?\n",
    "\n",
    "For the capstone, my variables or features of interest are the words that represent risk factors which signify trigger words for early intervention in mental health specifically depression\n",
    "\n",
    "### What outliers did you remove?\n",
    "\n",
    "In the context of textual parsing, by limiting word frequency to more than 2 occurrences and using trigrams(three-word pairs), any word combinations that did not meet that criteria were outliers.\n",
    "\n",
    "### What types of data imputation did you perform?\n",
    "\n",
    "Extending the stopwords list after visual examination of the postings was a form of data imputation that was performed. Uppercase was converted to lowercase and accents in words were stripped. During scraping, html tags and scripting elements were stripped."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-23T18:55:50.412982Z",
     "start_time": "2018-05-23T18:55:49.936915Z"
    }
   },
   "outputs": [],
   "source": [
    "# General imports for pickling and evaluating runtime performance\n",
    "import pickle\n",
    "import time\n",
    "\n",
    "# Sklearn imports both text preprocessing and sklearn's NLP models\n",
    "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
    "from sklearn.decomposition import LatentDirichletAllocation\n",
    "\n",
    "#imports for LDA topic model visualisation\n",
    "from __future__ import print_function\n",
    "import pyLDAvis\n",
    "import pyLDAvis.sklearn\n",
    "pyLDAvis.enable_notebook()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.1 Reading Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-23T18:55:53.338593Z",
     "start_time": "2018-05-23T18:55:53.302562Z"
    }
   },
   "outputs": [],
   "source": [
    "# Read scraped data into pandas dataframe\n",
    "postings = pd.read_excel(\"postings.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-23T18:55:54.378575Z",
     "start_time": "2018-05-23T18:55:54.363572Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>date</th>\n",
       "      <th>author</th>\n",
       "      <th>badge</th>\n",
       "      <th>likes</th>\n",
       "      <th>content</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>['3 March 2018']</td>\n",
       "      <td>Doolhof</td>\n",
       "      <td>Community Champion</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Right now I feel like I don't have the energy ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>['3 March 2018']</td>\n",
       "      <td>quirkywords</td>\n",
       "      <td>Community Champion</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Mrs dool.\\nI am sending you a big reassuring h...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>['3 March 2018']</td>\n",
       "      <td>Summer Rose</td>\n",
       "      <td>Valued Contributor</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Hi Doolhof\\nI'm sorry that you feel so low.  Y...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>['4 March 2018']</td>\n",
       "      <td>Doolhof</td>\n",
       "      <td>Community Champion</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Hi Quirky,\\nThanks for the virtual hug, I need...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>['4 March 2018']</td>\n",
       "      <td>Doolhof</td>\n",
       "      <td>Community Champion</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Hi Summer Rose,\\nThanks for your encouragement...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               date       author               badge  likes  \\\n",
       "1  ['3 March 2018']      Doolhof  Community Champion    4.0   \n",
       "2  ['3 March 2018']  quirkywords  Community Champion    3.0   \n",
       "3  ['3 March 2018']  Summer Rose  Valued Contributor    2.0   \n",
       "4  ['4 March 2018']      Doolhof  Community Champion    2.0   \n",
       "5  ['4 March 2018']      Doolhof  Community Champion    3.0   \n",
       "\n",
       "                                             content  \n",
       "1  Right now I feel like I don't have the energy ...  \n",
       "2  Mrs dool.\\nI am sending you a big reassuring h...  \n",
       "3  Hi Doolhof\\nI'm sorry that you feel so low.  Y...  \n",
       "4  Hi Quirky,\\nThanks for the virtual hug, I need...  \n",
       "5  Hi Summer Rose,\\nThanks for your encouragement...  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# only interested in the content of the postings at the moment so in our first iteration, EDA will focus on the posting content only.\n",
    "# Scraping was done more broadly as we could factor in likes, badge and use the dates for other modelling in later stages.\n",
    "postings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-23T18:55:55.753380Z",
     "start_time": "2018-05-23T18:55:55.748412Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 5)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "postings.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 150 posts from one of the discussion threads in the beyondblue mental health forum's depression subforum. This is a preliminary dataset and we will be expanding our dataset in the next iteration."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.2 Text Preprocessing and Normalisation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looked at the posts from within Excel and decided that we will only preprocess the post content for now. The dates can be dealt with when we want to perform any time-series modelling later. We scraped it given our final goal is geared towards an application where time and order of conversational flow matter. The rest of the fields besides content and date are pretty clean due to the html tag stripping done as part of the Beautiful Soup scraping process. So the EDA now focuses on the content of the posting...\n",
    "\n",
    "Initial exploration of the text data was done by visual examination of each individual posts in the excel spreadsheet and we found that there were words that could be excluded in addition to the standard stopwords list in NLTK library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-23T18:55:59.462574Z",
     "start_time": "2018-05-23T18:55:59.454603Z"
    }
   },
   "outputs": [],
   "source": [
    "# extending standard NLTK library's english stopwords to include other words like pronouns and meaningless words from visual examination in Excel\n",
    "stopwords = nltk.corpus.stopwords.words('english')\n",
    "newStopWords = [\"just\", \"like\", \"pamela\",\"karen\",\"dools\",\"hi\",\"hello\",\"chloe\",\"pamelar\",\"pammy\",\"mrs\", \"mr\", \"quirky\", \"doolhof\", \\\n",
    "                \"summer\", \"rose\",\"demonblaster\",\"quirkywords\",\"db\",\"Shell\",\"etc\",\"D\",\"Shelley\", \"anne\",\"Quercus\",\"Nat\",\"Dr\",\"PamelaR\",\"Bev\",\"Mary\", \\\n",
    "               \"Agapanthus\",\"Claire\", \"Weekes\", \"Paul\",\"blondguy\",\"em\",\"BB\",\"grandy\",\"TonyWK\",\"Tony\",\"Ggrand\",\"white\", \"knight\",\"Mam\", \\\n",
    "                \"Laters\",\"birdy\", \"Hiya\",\"Croix\", \"Croix's\",\"White\",\"Knight\",\"SN\",\"DB\",\"Queensland\",\"Doolsy\",\"bye\",\"Hi/bye\",\"Deebi\",\"Weetbix\", \\\n",
    "                \"Willow\", \"(WW)\",\"Chloe\", \"Chlo\",\"BTW\"]\n",
    "stopwords.extend(newStopWords)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-23T18:56:00.210576Z",
     "start_time": "2018-05-23T18:56:00.200575Z"
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   },
   "outputs": [],
   "source": [
    "def getcvt_freq_words(sparse_counts, columns):\n",
    "   # X_all is a sparse matrix, so sum() returns a 'matrix' datatype ...\n",
    "   #   which we then convert into a 1-D ndarray for sorting\n",
    "   word_counts = np.asarray(X_cvt.sum(axis=0)).reshape(-1)\n",
    "\n",
    "   # argsort() returns smallest first, so we reverse the result\n",
    "   largest_count_indices = word_counts.argsort()[::-1]\n",
    "\n",
    "   # pretty-print the results! Remember to always ask whether they make sense ...\n",
    "   freq_words = pd.Series(word_counts[largest_count_indices],\n",
    "                          index=columns[largest_count_indices])\n",
    "\n",
    "   return freq_words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-23T18:56:00.929588Z",
     "start_time": "2018-05-23T18:56:00.918609Z"
    }
   },
   "outputs": [],
   "source": [
    "def gettfidf_freq_words(sparse_counts, columns):\n",
    "   # X_all is a sparse matrix, so sum() returns a 'matrix' datatype ...\n",
    "   #   which we then convert into a 1-D ndarray for sorting\n",
    "   word_counts = np.asarray(X_tfidf.sum(axis=0)).reshape(-1)\n",
    "\n",
    "   # argsort() returns smallest first, so we reverse the result\n",
    "   largest_count_indices = word_counts.argsort()[::-1]\n",
    "\n",
    "   # pretty-print the results! Remember to always ask whether they make sense ...\n",
    "   freq_words = pd.Series(word_counts[largest_count_indices],\n",
    "                          index=columns[largest_count_indices])\n",
    "\n",
    "   return freq_words"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.3 ETL: Transforming Text into Word Vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-23T18:56:03.341571Z",
     "start_time": "2018-05-23T18:56:03.338572Z"
    }
   },
   "outputs": [],
   "source": [
    "# ETL algorithms to normalise text into word vectors - testing countvectorizer and tfidfvectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-23T18:59:39.700133Z",
     "start_time": "2018-05-23T18:59:39.634136Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(150, 65)\n",
      "Requires 9750 ints to do a .toarray()!\n"
     ]
    }
   ],
   "source": [
    "#CountVectorizer\n",
    "cvt_vectorizer = CountVectorizer(analyzer=\"word\", stop_words= stopwords, ngram_range=(3,3),min_df = 2, max_df=0.5, max_features=10000, lowercase=True, \\\n",
    "                      strip_accents=\"unicode\")\n",
    "X_cvt =  cvt_vectorizer.fit_transform(postings.content)\n",
    "columns = np.array(cvt_vectorizer.get_feature_names())          # ndarray (for indexing below)\n",
    "print(X_cvt.shape)\n",
    "print(\"Requires {} ints to do a .toarray()!\".format(X_cvt.shape[0] * X_cvt.shape[1]))\n",
    "freq_words_cvt = getcvt_freq_words(X_cvt, columns)\n",
    "freq_words_cvt.to_csv(\"freq_words_cvt.csv\")\n",
    "freq_words_cvt = pd.read_csv(\"freq_words_cvt.csv\", header=None, names=[\"word\", \"count\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-23T18:59:40.891136Z",
     "start_time": "2018-05-23T18:59:40.876135Z"
    }
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>word</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mental health issues</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>cuddling black dog</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sending virtual hugs</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>darn black dog</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>going try make</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>sorry hear struggling</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>dance around house</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>positivity motivation humour</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>help feel better</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>hope okay today</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>feel cheerful happy</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>hoping today better</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>got nan dog</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>hour drive away</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>good day going</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>huge gum leaves</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>go round emotions</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>fight tired unwell</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>feeling really depressed</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>write gratitude journal</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>explain people around</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>feel bit better</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>laughter good soul</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>exhausting full stop</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>enjoy time family</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>dog feral cat</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>depression explain people</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>country fire service</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>channel end game</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>cat hanging upside</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>loads virtual hugs</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>love go camping</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>wave everyone else</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>wanting run away</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>unwell tired trying</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>tired unwell tired</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>time depression explain</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>things know help</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>thanks much comments</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>thanks going try</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>taken long time</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>snarling german shepherd</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>self help books</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>scenery driving along</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>said really enjoyed</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>reading self help</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>ray ct scan</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>please ever give</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>picnic drs office</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>pain mental health</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>outside look stars</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>oh well hope</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>need find works</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>need find ways</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>need fight depression</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>much kind words</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>merry go round</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>many wonderful people</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>wondering time depression</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>ants huge gum</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>65 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            word  count\n",
       "0           mental health issues      8\n",
       "1             cuddling black dog      6\n",
       "2           sending virtual hugs      5\n",
       "3                 darn black dog      4\n",
       "4                 going try make      4\n",
       "5          sorry hear struggling      3\n",
       "6             dance around house      3\n",
       "7   positivity motivation humour      3\n",
       "8               help feel better      2\n",
       "9                hope okay today      2\n",
       "10           feel cheerful happy      2\n",
       "11           hoping today better      2\n",
       "12                   got nan dog      2\n",
       "13               hour drive away      2\n",
       "14                good day going      2\n",
       "15               huge gum leaves      2\n",
       "16             go round emotions      2\n",
       "17            fight tired unwell      2\n",
       "18      feeling really depressed      2\n",
       "19       write gratitude journal      2\n",
       "20         explain people around      2\n",
       "21               feel bit better      2\n",
       "22            laughter good soul      2\n",
       "23          exhausting full stop      2\n",
       "24             enjoy time family      2\n",
       "25                 dog feral cat      2\n",
       "26     depression explain people      2\n",
       "27          country fire service      2\n",
       "28              channel end game      2\n",
       "29            cat hanging upside      2\n",
       "..                           ...    ...\n",
       "35            loads virtual hugs      2\n",
       "36               love go camping      2\n",
       "37            wave everyone else      2\n",
       "38              wanting run away      2\n",
       "39           unwell tired trying      2\n",
       "40            tired unwell tired      2\n",
       "41       time depression explain      2\n",
       "42              things know help      2\n",
       "43          thanks much comments      2\n",
       "44              thanks going try      2\n",
       "45               taken long time      2\n",
       "46      snarling german shepherd      2\n",
       "47               self help books      2\n",
       "48         scenery driving along      2\n",
       "49           said really enjoyed      2\n",
       "50             reading self help      2\n",
       "51                   ray ct scan      2\n",
       "52              please ever give      2\n",
       "53             picnic drs office      2\n",
       "54            pain mental health      2\n",
       "55            outside look stars      2\n",
       "56                  oh well hope      2\n",
       "57               need find works      2\n",
       "58                need find ways      2\n",
       "59         need fight depression      2\n",
       "60               much kind words      2\n",
       "61                merry go round      2\n",
       "62         many wonderful people      2\n",
       "63     wondering time depression      2\n",
       "64                 ants huge gum      2\n",
       "\n",
       "[65 rows x 2 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "freq_words_cvt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-23T18:59:29.899142Z",
     "start_time": "2018-05-23T18:59:29.827134Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(150, 65)\n",
      "Requires 9750 ints to do a .toarray()!\n"
     ]
    }
   ],
   "source": [
    "# tfidf vectorizer\n",
    "tfidf_vectorizer = TfidfVectorizer(analyzer=\"word\", stop_words= stopwords, ngram_range=(3,3),max_df=0.5, max_features=10000,\n",
    "                             min_df=2,\n",
    "                             use_idf=True)\n",
    "X_tfidf =  tfidf_vectorizer.fit_transform(postings.content)\n",
    "columns = np.array(tfidf_vectorizer.get_feature_names())          # ndarray (for indexing below)\n",
    "print(X_tfidf.shape)\n",
    "print(\"Requires {} ints to do a .toarray()!\".format(X_tfidf.shape[0] * X_tfidf.shape[1]))\n",
    "freq_words_tfidf = gettfidf_freq_words(X_tfidf, columns)\n",
    "freq_words_tfidf.to_csv(\"freq_words_tfidf.csv\")\n",
    "freq_words_tfidf = pd.read_csv(\"freq_words_tfidf.csv\", header=None, names=[\"word\", \"count\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-23T18:59:46.695704Z",
     "start_time": "2018-05-23T18:59:46.674707Z"
    }
   },
   "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>word</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mental health issues</td>\n",
       "      <td>5.005902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sending virtual hugs</td>\n",
       "      <td>4.049465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>cuddling black dog</td>\n",
       "      <td>3.846836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>positivity motivation humour</td>\n",
       "      <td>2.685494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>sorry hear struggling</td>\n",
       "      <td>2.266668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>dance around house</td>\n",
       "      <td>2.192610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>darn black dog</td>\n",
       "      <td>2.111416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>got nan dog</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>love go camping</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>enjoy time family</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>going try make</td>\n",
       "      <td>1.911019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>feel cheerful happy</td>\n",
       "      <td>1.789786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>need fight depression</td>\n",
       "      <td>1.789786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>hoping today better</td>\n",
       "      <td>1.728078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>better kind thoughts</td>\n",
       "      <td>1.728078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>many wonderful people</td>\n",
       "      <td>1.707107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>much kind words</td>\n",
       "      <td>1.707107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>loads virtual hugs</td>\n",
       "      <td>1.707107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>picnic drs office</td>\n",
       "      <td>1.707107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>please ever give</td>\n",
       "      <td>1.707107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>channel end game</td>\n",
       "      <td>1.707107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>reading self help</td>\n",
       "      <td>1.707107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>wave everyone else</td>\n",
       "      <td>1.707107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>wanting run away</td>\n",
       "      <td>1.707107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>said really enjoyed</td>\n",
       "      <td>1.707107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>care good lady</td>\n",
       "      <td>1.707107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>self help books</td>\n",
       "      <td>1.707107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>ray ct scan</td>\n",
       "      <td>1.707107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>cat hanging upside</td>\n",
       "      <td>1.707107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>need find ways</td>\n",
       "      <td>1.619795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>oh well hope</td>\n",
       "      <td>1.435185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>exhausting full stop</td>\n",
       "      <td>1.414214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>taken long time</td>\n",
       "      <td>1.408248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>need find works</td>\n",
       "      <td>1.408248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>snarling german shepherd</td>\n",
       "      <td>1.380598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>ants huge gum</td>\n",
       "      <td>1.311448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>huge gum leaves</td>\n",
       "      <td>1.311448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>feeling really depressed</td>\n",
       "      <td>1.295704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>help feel better</td>\n",
       "      <td>1.233921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>country fire service</td>\n",
       "      <td>1.233921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>pain mental health</td>\n",
       "      <td>1.233921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>merry go round</td>\n",
       "      <td>1.207107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>go round emotions</td>\n",
       "      <td>1.207107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>feel bit better</td>\n",
       "      <td>1.207107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>things know help</td>\n",
       "      <td>1.159487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>write gratitude journal</td>\n",
       "      <td>1.159487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>good day going</td>\n",
       "      <td>1.159487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>thanks going try</td>\n",
       "      <td>1.152716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>laughter good soul</td>\n",
       "      <td>1.117277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>thanks much comments</td>\n",
       "      <td>1.076353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>black dog right</td>\n",
       "      <td>1.072175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>outside look stars</td>\n",
       "      <td>1.062641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>fight tired unwell</td>\n",
       "      <td>1.043442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>tired unwell tired</td>\n",
       "      <td>1.043442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>unwell tired trying</td>\n",
       "      <td>1.043442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>wondering time depression</td>\n",
       "      <td>0.908248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>explain people around</td>\n",
       "      <td>0.908248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>depression explain people</td>\n",
       "      <td>0.908248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>time depression explain</td>\n",
       "      <td>0.908248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>hour drive away</td>\n",
       "      <td>0.877018</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>65 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            word     count\n",
       "0           mental health issues  5.005902\n",
       "1           sending virtual hugs  4.049465\n",
       "2             cuddling black dog  3.846836\n",
       "3   positivity motivation humour  2.685494\n",
       "4          sorry hear struggling  2.266668\n",
       "5             dance around house  2.192610\n",
       "6                 darn black dog  2.111416\n",
       "7                    got nan dog  2.000000\n",
       "8                love go camping  2.000000\n",
       "9              enjoy time family  2.000000\n",
       "10                going try make  1.911019\n",
       "11           feel cheerful happy  1.789786\n",
       "12         need fight depression  1.789786\n",
       "13           hoping today better  1.728078\n",
       "14          better kind thoughts  1.728078\n",
       "15         many wonderful people  1.707107\n",
       "16               much kind words  1.707107\n",
       "17            loads virtual hugs  1.707107\n",
       "18             picnic drs office  1.707107\n",
       "19              please ever give  1.707107\n",
       "20              channel end game  1.707107\n",
       "21             reading self help  1.707107\n",
       "22            wave everyone else  1.707107\n",
       "23              wanting run away  1.707107\n",
       "24           said really enjoyed  1.707107\n",
       "25                care good lady  1.707107\n",
       "26               self help books  1.707107\n",
       "27                   ray ct scan  1.707107\n",
       "28            cat hanging upside  1.707107\n",
       "29                need find ways  1.619795\n",
       "..                           ...       ...\n",
       "35                  oh well hope  1.435185\n",
       "36          exhausting full stop  1.414214\n",
       "37               taken long time  1.408248\n",
       "38               need find works  1.408248\n",
       "39      snarling german shepherd  1.380598\n",
       "40                 ants huge gum  1.311448\n",
       "41               huge gum leaves  1.311448\n",
       "42      feeling really depressed  1.295704\n",
       "43              help feel better  1.233921\n",
       "44          country fire service  1.233921\n",
       "45            pain mental health  1.233921\n",
       "46                merry go round  1.207107\n",
       "47             go round emotions  1.207107\n",
       "48               feel bit better  1.207107\n",
       "49              things know help  1.159487\n",
       "50       write gratitude journal  1.159487\n",
       "51                good day going  1.159487\n",
       "52              thanks going try  1.152716\n",
       "53            laughter good soul  1.117277\n",
       "54          thanks much comments  1.076353\n",
       "55               black dog right  1.072175\n",
       "56            outside look stars  1.062641\n",
       "57            fight tired unwell  1.043442\n",
       "58            tired unwell tired  1.043442\n",
       "59           unwell tired trying  1.043442\n",
       "60     wondering time depression  0.908248\n",
       "61         explain people around  0.908248\n",
       "62     depression explain people  0.908248\n",
       "63       time depression explain  0.908248\n",
       "64               hour drive away  0.877018\n",
       "\n",
       "[65 rows x 2 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "freq_words_tfidf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3 Modelling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.1 Topic Modelling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1.1 LDA (Latent Dirichlet Allocation) topic modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-23T18:59:57.705661Z",
     "start_time": "2018-05-23T18:59:53.323065Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.0125    , 0.0125    , 0.0125    , ..., 0.0125    , 0.0125    ,\n",
       "        0.0125    ],\n",
       "       [0.05      , 0.05      , 0.05      , ..., 0.05      , 0.05      ,\n",
       "        0.05      ],\n",
       "       [0.0125    , 0.0125    , 0.0125    , ..., 0.0125    , 0.0125    ,\n",
       "        0.0125    ],\n",
       "       ...,\n",
       "       [0.01666667, 0.01666667, 0.68333333, ..., 0.01666667, 0.01666667,\n",
       "        0.01666667],\n",
       "       [0.025     , 0.025     , 0.025     , ..., 0.025     , 0.525     ,\n",
       "        0.025     ],\n",
       "       [0.05      , 0.05      , 0.05      , ..., 0.05      , 0.05      ,\n",
       "        0.05      ]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#LDA topic model fit based on CountVectorizer \n",
    "\n",
    "lda_cvt = LatentDirichletAllocation(n_components=20, random_state=0, n_jobs=-1, learning_method=\"online\")\n",
    "lda_cvt.fit_transform(X_cvt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-20T17:02:14.707903Z",
     "start_time": "2018-05-20T17:02:14.698894Z"
    }
   },
   "outputs": [],
   "source": [
    "#import GridSearchCV\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-20T17:02:21.756192Z",
     "start_time": "2018-05-20T17:02:14.713892Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\decomposition\\online_lda.py:536: DeprecationWarning: The default value for 'learning_method' will be changed from 'online' to 'batch' in the release 0.20. This warning was introduced in 0.18.\n",
      "  DeprecationWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\decomposition\\online_lda.py:536: DeprecationWarning: The default value for 'learning_method' will be changed from 'online' to 'batch' in the release 0.20. This warning was introduced in 0.18.\n",
      "  DeprecationWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\decomposition\\online_lda.py:536: DeprecationWarning: The default value for 'learning_method' will be changed from 'online' to 'batch' in the release 0.20. This warning was introduced in 0.18.\n",
      "  DeprecationWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\decomposition\\online_lda.py:536: DeprecationWarning: The default value for 'learning_method' will be changed from 'online' to 'batch' in the release 0.20. This warning was introduced in 0.18.\n",
      "  DeprecationWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\decomposition\\online_lda.py:536: DeprecationWarning: The default value for 'learning_method' will be changed from 'online' to 'batch' in the release 0.20. This warning was introduced in 0.18.\n",
      "  DeprecationWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\decomposition\\online_lda.py:536: DeprecationWarning: The default value for 'learning_method' will be changed from 'online' to 'batch' in the release 0.20. This warning was introduced in 0.18.\n",
      "  DeprecationWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\decomposition\\online_lda.py:536: DeprecationWarning: The default value for 'learning_method' will be changed from 'online' to 'batch' in the release 0.20. This warning was introduced in 0.18.\n",
      "  DeprecationWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\decomposition\\online_lda.py:536: DeprecationWarning: The default value for 'learning_method' will be changed from 'online' to 'batch' in the release 0.20. This warning was introduced in 0.18.\n",
      "  DeprecationWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\decomposition\\online_lda.py:536: DeprecationWarning: The default value for 'learning_method' will be changed from 'online' to 'batch' in the release 0.20. This warning was introduced in 0.18.\n",
      "  DeprecationWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\decomposition\\online_lda.py:536: DeprecationWarning: The default value for 'learning_method' will be changed from 'online' to 'batch' in the release 0.20. This warning was introduced in 0.18.\n",
      "  DeprecationWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\decomposition\\online_lda.py:536: DeprecationWarning: The default value for 'learning_method' will be changed from 'online' to 'batch' in the release 0.20. This warning was introduced in 0.18.\n",
      "  DeprecationWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\decomposition\\online_lda.py:536: DeprecationWarning: The default value for 'learning_method' will be changed from 'online' to 'batch' in the release 0.20. This warning was introduced in 0.18.\n",
      "  DeprecationWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\decomposition\\online_lda.py:536: DeprecationWarning: The default value for 'learning_method' will be changed from 'online' to 'batch' in the release 0.20. This warning was introduced in 0.18.\n",
      "  DeprecationWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\decomposition\\online_lda.py:536: DeprecationWarning: The default value for 'learning_method' will be changed from 'online' to 'batch' in the release 0.20. This warning was introduced in 0.18.\n",
      "  DeprecationWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\decomposition\\online_lda.py:536: DeprecationWarning: The default value for 'learning_method' will be changed from 'online' to 'batch' in the release 0.20. This warning was introduced in 0.18.\n",
      "  DeprecationWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\decomposition\\online_lda.py:536: DeprecationWarning: The default value for 'learning_method' will be changed from 'online' to 'batch' in the release 0.20. This warning was introduced in 0.18.\n",
      "  DeprecationWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\decomposition\\online_lda.py:536: DeprecationWarning: The default value for 'learning_method' will be changed from 'online' to 'batch' in the release 0.20. This warning was introduced in 0.18.\n",
      "  DeprecationWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\decomposition\\online_lda.py:536: DeprecationWarning: The default value for 'learning_method' will be changed from 'online' to 'batch' in the release 0.20. This warning was introduced in 0.18.\n",
      "  DeprecationWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\decomposition\\online_lda.py:536: DeprecationWarning: The default value for 'learning_method' will be changed from 'online' to 'batch' in the release 0.20. This warning was introduced in 0.18.\n",
      "  DeprecationWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\decomposition\\online_lda.py:536: DeprecationWarning: The default value for 'learning_method' will be changed from 'online' to 'batch' in the release 0.20. This warning was introduced in 0.18.\n",
      "  DeprecationWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\decomposition\\online_lda.py:536: DeprecationWarning: The default value for 'learning_method' will be changed from 'online' to 'batch' in the release 0.20. This warning was introduced in 0.18.\n",
      "  DeprecationWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\decomposition\\online_lda.py:536: DeprecationWarning: The default value for 'learning_method' will be changed from 'online' to 'batch' in the release 0.20. This warning was introduced in 0.18.\n",
      "  DeprecationWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\decomposition\\online_lda.py:536: DeprecationWarning: The default value for 'learning_method' will be changed from 'online' to 'batch' in the release 0.20. This warning was introduced in 0.18.\n",
      "  DeprecationWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\decomposition\\online_lda.py:536: DeprecationWarning: The default value for 'learning_method' will be changed from 'online' to 'batch' in the release 0.20. This warning was introduced in 0.18.\n",
      "  DeprecationWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\decomposition\\online_lda.py:536: DeprecationWarning: The default value for 'learning_method' will be changed from 'online' to 'batch' in the release 0.20. This warning was introduced in 0.18.\n",
      "  DeprecationWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\decomposition\\online_lda.py:536: DeprecationWarning: The default value for 'learning_method' will be changed from 'online' to 'batch' in the release 0.20. This warning was introduced in 0.18.\n",
      "  DeprecationWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\decomposition\\online_lda.py:536: DeprecationWarning: The default value for 'learning_method' will be changed from 'online' to 'batch' in the release 0.20. This warning was introduced in 0.18.\n",
      "  DeprecationWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\decomposition\\online_lda.py:536: DeprecationWarning: The default value for 'learning_method' will be changed from 'online' to 'batch' in the release 0.20. This warning was introduced in 0.18.\n",
      "  DeprecationWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\decomposition\\online_lda.py:536: DeprecationWarning: The default value for 'learning_method' will be changed from 'online' to 'batch' in the release 0.20. This warning was introduced in 0.18.\n",
      "  DeprecationWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\decomposition\\online_lda.py:536: DeprecationWarning: The default value for 'learning_method' will be changed from 'online' to 'batch' in the release 0.20. This warning was introduced in 0.18.\n",
      "  DeprecationWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\decomposition\\online_lda.py:536: DeprecationWarning: The default value for 'learning_method' will be changed from 'online' to 'batch' in the release 0.20. This warning was introduced in 0.18.\n",
      "  DeprecationWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\decomposition\\online_lda.py:536: DeprecationWarning: The default value for 'learning_method' will be changed from 'online' to 'batch' in the release 0.20. This warning was introduced in 0.18.\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\decomposition\\online_lda.py:536: DeprecationWarning: The default value for 'learning_method' will be changed from 'online' to 'batch' in the release 0.20. This warning was introduced in 0.18.\n",
      "  DeprecationWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\decomposition\\online_lda.py:536: DeprecationWarning: The default value for 'learning_method' will be changed from 'online' to 'batch' in the release 0.20. This warning was introduced in 0.18.\n",
      "  DeprecationWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\decomposition\\online_lda.py:536: DeprecationWarning: The default value for 'learning_method' will be changed from 'online' to 'batch' in the release 0.20. This warning was introduced in 0.18.\n",
      "  DeprecationWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\decomposition\\online_lda.py:536: DeprecationWarning: The default value for 'learning_method' will be changed from 'online' to 'batch' in the release 0.20. This warning was introduced in 0.18.\n",
      "  DeprecationWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\decomposition\\online_lda.py:536: DeprecationWarning: The default value for 'learning_method' will be changed from 'online' to 'batch' in the release 0.20. This warning was introduced in 0.18.\n",
      "  DeprecationWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\decomposition\\online_lda.py:536: DeprecationWarning: The default value for 'learning_method' will be changed from 'online' to 'batch' in the release 0.20. This warning was introduced in 0.18.\n",
      "  DeprecationWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\decomposition\\online_lda.py:536: DeprecationWarning: The default value for 'learning_method' will be changed from 'online' to 'batch' in the release 0.20. This warning was introduced in 0.18.\n",
      "  DeprecationWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\decomposition\\online_lda.py:536: DeprecationWarning: The default value for 'learning_method' will be changed from 'online' to 'batch' in the release 0.20. This warning was introduced in 0.18.\n",
      "  DeprecationWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\decomposition\\online_lda.py:536: DeprecationWarning: The default value for 'learning_method' will be changed from 'online' to 'batch' in the release 0.20. This warning was introduced in 0.18.\n",
      "  DeprecationWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\decomposition\\online_lda.py:536: DeprecationWarning: The default value for 'learning_method' will be changed from 'online' to 'batch' in the release 0.20. This warning was introduced in 0.18.\n",
      "  DeprecationWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\decomposition\\online_lda.py:536: DeprecationWarning: The default value for 'learning_method' will be changed from 'online' to 'batch' in the release 0.20. This warning was introduced in 0.18.\n",
      "  DeprecationWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\decomposition\\online_lda.py:536: DeprecationWarning: The default value for 'learning_method' will be changed from 'online' to 'batch' in the release 0.20. This warning was introduced in 0.18.\n",
      "  DeprecationWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\decomposition\\online_lda.py:536: DeprecationWarning: The default value for 'learning_method' will be changed from 'online' to 'batch' in the release 0.20. This warning was introduced in 0.18.\n",
      "  DeprecationWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\decomposition\\online_lda.py:536: DeprecationWarning: The default value for 'learning_method' will be changed from 'online' to 'batch' in the release 0.20. This warning was introduced in 0.18.\n",
      "  DeprecationWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\decomposition\\online_lda.py:536: DeprecationWarning: The default value for 'learning_method' will be changed from 'online' to 'batch' in the release 0.20. This warning was introduced in 0.18.\n",
      "  DeprecationWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\decomposition\\online_lda.py:536: DeprecationWarning: The default value for 'learning_method' will be changed from 'online' to 'batch' in the release 0.20. This warning was introduced in 0.18.\n",
      "  DeprecationWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\decomposition\\online_lda.py:536: DeprecationWarning: The default value for 'learning_method' will be changed from 'online' to 'batch' in the release 0.20. This warning was introduced in 0.18.\n",
      "  DeprecationWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\decomposition\\online_lda.py:536: DeprecationWarning: The default value for 'learning_method' will be changed from 'online' to 'batch' in the release 0.20. This warning was introduced in 0.18.\n",
      "  DeprecationWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\decomposition\\online_lda.py:536: DeprecationWarning: The default value for 'learning_method' will be changed from 'online' to 'batch' in the release 0.20. This warning was introduced in 0.18.\n",
      "  DeprecationWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\decomposition\\online_lda.py:536: DeprecationWarning: The default value for 'learning_method' will be changed from 'online' to 'batch' in the release 0.20. This warning was introduced in 0.18.\n",
      "  DeprecationWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\decomposition\\online_lda.py:536: DeprecationWarning: The default value for 'learning_method' will be changed from 'online' to 'batch' in the release 0.20. This warning was introduced in 0.18.\n",
      "  DeprecationWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\decomposition\\online_lda.py:536: DeprecationWarning: The default value for 'learning_method' will be changed from 'online' to 'batch' in the release 0.20. This warning was introduced in 0.18.\n",
      "  DeprecationWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\decomposition\\online_lda.py:536: DeprecationWarning: The default value for 'learning_method' will be changed from 'online' to 'batch' in the release 0.20. This warning was introduced in 0.18.\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=None, error_score='raise',\n",
       "       estimator=LatentDirichletAllocation(batch_size=128, doc_topic_prior=None,\n",
       "             evaluate_every=-1, learning_decay=0.7, learning_method=None,\n",
       "             learning_offset=10.0, max_doc_update_iter=100, max_iter=10,\n",
       "             mean_change_tol=0.001, n_components=10, n_jobs=1,\n",
       "             n_topics=None, perp_tol=0.1, random_state=None,\n",
       "             topic_word_prior=None, total_samples=1000000.0, verbose=0),\n",
       "       fit_params=None, iid=True, n_jobs=1,\n",
       "       param_grid={'n_components': [2, 5, 7, 10, 20, 30], 'learning_decay': [0.5, 0.7, 0.9]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring=None, verbose=0)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define Search Param\n",
    "search_params = {'n_components': [2,5,7,10,20,30], 'learning_decay': [.5, .7, .9]}\n",
    "\n",
    "# Init the Model\n",
    "lda = LatentDirichletAllocation()\n",
    "\n",
    "# Init Grid Search Class\n",
    "model = GridSearchCV(lda, param_grid=search_params)\n",
    "\n",
    "# Do the Grid Search for CVT\n",
    "model.fit(X_cvt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-20T17:02:21.768199Z",
     "start_time": "2018-05-20T17:02:21.761197Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'learning_decay': 0.9, 'n_components': 2}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Best parameters for cvt\n",
    "model.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-23T19:00:09.237664Z",
     "start_time": "2018-05-23T19:00:04.974628Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.0125    , 0.0125    , 0.0125    , ..., 0.0125    , 0.0125    ,\n",
       "        0.0125    ],\n",
       "       [0.05      , 0.05      , 0.05      , ..., 0.05      , 0.05      ,\n",
       "        0.05      ],\n",
       "       [0.0125    , 0.0125    , 0.0125    , ..., 0.0125    , 0.0125    ,\n",
       "        0.0125    ],\n",
       "       ...,\n",
       "       [0.01666667, 0.01666667, 0.01666667, ..., 0.01666667, 0.01666667,\n",
       "        0.01666667],\n",
       "       [0.025     , 0.025     , 0.025     , ..., 0.025     , 0.525     ,\n",
       "        0.025     ],\n",
       "       [0.05      , 0.05      , 0.05      , ..., 0.05      , 0.05      ,\n",
       "        0.05      ]])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Optimised LDA topic model fit based on CountVectorizer \n",
    "\n",
    "lda_cvt = LatentDirichletAllocation(n_components=20, random_state=0, n_jobs=-1, learning_method=\"online\", learning_decay=0.9)\n",
    "lda_cvt.fit_transform(X_cvt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-23T19:00:18.535514Z",
     "start_time": "2018-05-23T19:00:14.221267Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.01837593, 0.01837593, 0.01837593, ..., 0.01837593, 0.01837593,\n",
       "        0.01837593],\n",
       "       [0.05      , 0.05      , 0.05      , ..., 0.05      , 0.05      ,\n",
       "        0.05      ],\n",
       "       [0.01830127, 0.01830127, 0.01830127, ..., 0.01830127, 0.01830127,\n",
       "        0.01830127],\n",
       "       ...,\n",
       "       [0.02071068, 0.02071068, 0.02071068, ..., 0.02071068, 0.02071068,\n",
       "        0.02071068],\n",
       "       [0.025     , 0.025     , 0.025     , ..., 0.025     , 0.525     ,\n",
       "        0.025     ],\n",
       "       [0.05      , 0.05      , 0.05      , ..., 0.05      , 0.05      ,\n",
       "        0.05      ]])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#LDA topic model fit based on tfidf_vectorizer \n",
    "lda_tfidf = LatentDirichletAllocation(n_components=20, random_state=0, n_jobs=-1, learning_method='online')\n",
    "lda_tfidf.fit_transform(X_tfidf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-20T17:02:38.592321Z",
     "start_time": "2018-05-20T17:02:32.113324Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\decomposition\\online_lda.py:536: DeprecationWarning: The default value for 'learning_method' will be changed from 'online' to 'batch' in the release 0.20. This warning was introduced in 0.18.\n",
      "  DeprecationWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\decomposition\\online_lda.py:536: DeprecationWarning: The default value for 'learning_method' will be changed from 'online' to 'batch' in the release 0.20. This warning was introduced in 0.18.\n",
      "  DeprecationWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\decomposition\\online_lda.py:536: DeprecationWarning: The default value for 'learning_method' will be changed from 'online' to 'batch' in the release 0.20. This warning was introduced in 0.18.\n",
      "  DeprecationWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\decomposition\\online_lda.py:536: DeprecationWarning: The default value for 'learning_method' will be changed from 'online' to 'batch' in the release 0.20. This warning was introduced in 0.18.\n",
      "  DeprecationWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\decomposition\\online_lda.py:536: DeprecationWarning: The default value for 'learning_method' will be changed from 'online' to 'batch' in the release 0.20. This warning was introduced in 0.18.\n",
      "  DeprecationWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\decomposition\\online_lda.py:536: DeprecationWarning: The default value for 'learning_method' will be changed from 'online' to 'batch' in the release 0.20. This warning was introduced in 0.18.\n",
      "  DeprecationWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\decomposition\\online_lda.py:536: DeprecationWarning: The default value for 'learning_method' will be changed from 'online' to 'batch' in the release 0.20. This warning was introduced in 0.18.\n",
      "  DeprecationWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\decomposition\\online_lda.py:536: DeprecationWarning: The default value for 'learning_method' will be changed from 'online' to 'batch' in the release 0.20. This warning was introduced in 0.18.\n",
      "  DeprecationWarning)\n",
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      "  DeprecationWarning)\n",
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      "  DeprecationWarning)\n",
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      "  DeprecationWarning)\n",
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      "  DeprecationWarning)\n",
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      "  DeprecationWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\decomposition\\online_lda.py:536: DeprecationWarning: The default value for 'learning_method' will be changed from 'online' to 'batch' in the release 0.20. This warning was introduced in 0.18.\n",
      "  DeprecationWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\decomposition\\online_lda.py:536: DeprecationWarning: The default value for 'learning_method' will be changed from 'online' to 'batch' in the release 0.20. This warning was introduced in 0.18.\n",
      "  DeprecationWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\decomposition\\online_lda.py:536: DeprecationWarning: The default value for 'learning_method' will be changed from 'online' to 'batch' in the release 0.20. This warning was introduced in 0.18.\n",
      "  DeprecationWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\decomposition\\online_lda.py:536: DeprecationWarning: The default value for 'learning_method' will be changed from 'online' to 'batch' in the release 0.20. This warning was introduced in 0.18.\n",
      "  DeprecationWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\decomposition\\online_lda.py:536: DeprecationWarning: The default value for 'learning_method' will be changed from 'online' to 'batch' in the release 0.20. This warning was introduced in 0.18.\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\decomposition\\online_lda.py:536: DeprecationWarning: The default value for 'learning_method' will be changed from 'online' to 'batch' in the release 0.20. This warning was introduced in 0.18.\n",
      "  DeprecationWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\decomposition\\online_lda.py:536: DeprecationWarning: The default value for 'learning_method' will be changed from 'online' to 'batch' in the release 0.20. This warning was introduced in 0.18.\n",
      "  DeprecationWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\decomposition\\online_lda.py:536: DeprecationWarning: The default value for 'learning_method' will be changed from 'online' to 'batch' in the release 0.20. This warning was introduced in 0.18.\n",
      "  DeprecationWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\decomposition\\online_lda.py:536: DeprecationWarning: The default value for 'learning_method' will be changed from 'online' to 'batch' in the release 0.20. This warning was introduced in 0.18.\n",
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      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\decomposition\\online_lda.py:536: DeprecationWarning: The default value for 'learning_method' will be changed from 'online' to 'batch' in the release 0.20. This warning was introduced in 0.18.\n",
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      "  DeprecationWarning)\n",
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      "  DeprecationWarning)\n",
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      "  DeprecationWarning)\n",
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      "  DeprecationWarning)\n",
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      "  DeprecationWarning)\n",
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      "  DeprecationWarning)\n",
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      "  DeprecationWarning)\n",
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      "  DeprecationWarning)\n",
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      "  DeprecationWarning)\n",
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      "  DeprecationWarning)\n",
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      "  DeprecationWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\decomposition\\online_lda.py:536: DeprecationWarning: The default value for 'learning_method' will be changed from 'online' to 'batch' in the release 0.20. This warning was introduced in 0.18.\n",
      "  DeprecationWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\decomposition\\online_lda.py:536: DeprecationWarning: The default value for 'learning_method' will be changed from 'online' to 'batch' in the release 0.20. This warning was introduced in 0.18.\n",
      "  DeprecationWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\decomposition\\online_lda.py:536: DeprecationWarning: The default value for 'learning_method' will be changed from 'online' to 'batch' in the release 0.20. This warning was introduced in 0.18.\n",
      "  DeprecationWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\decomposition\\online_lda.py:536: DeprecationWarning: The default value for 'learning_method' will be changed from 'online' to 'batch' in the release 0.20. This warning was introduced in 0.18.\n",
      "  DeprecationWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\decomposition\\online_lda.py:536: DeprecationWarning: The default value for 'learning_method' will be changed from 'online' to 'batch' in the release 0.20. This warning was introduced in 0.18.\n",
      "  DeprecationWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\decomposition\\online_lda.py:536: DeprecationWarning: The default value for 'learning_method' will be changed from 'online' to 'batch' in the release 0.20. This warning was introduced in 0.18.\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=None, error_score='raise',\n",
       "       estimator=LatentDirichletAllocation(batch_size=128, doc_topic_prior=None,\n",
       "             evaluate_every=-1, learning_decay=0.7, learning_method=None,\n",
       "             learning_offset=10.0, max_doc_update_iter=100, max_iter=10,\n",
       "             mean_change_tol=0.001, n_components=10, n_jobs=1,\n",
       "             n_topics=None, perp_tol=0.1, random_state=None,\n",
       "             topic_word_prior=None, total_samples=1000000.0, verbose=0),\n",
       "       fit_params=None, iid=True, n_jobs=1,\n",
       "       param_grid={'n_components': [2, 5, 7, 10, 20, 30], 'learning_decay': [0.5, 0.7, 0.9]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring=None, verbose=0)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define Search Param\n",
    "search_params = {'n_components': [2,5,7,10,20,30], 'learning_decay': [.5, .7, .9]}\n",
    "\n",
    "# Init the Model\n",
    "lda = LatentDirichletAllocation()\n",
    "\n",
    "# Init Grid Search Class\n",
    "model = GridSearchCV(lda, param_grid=search_params)\n",
    "\n",
    "# Do the Grid Search for tfidf\n",
    "model.fit(X_tfidf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-20T17:02:38.600324Z",
     "start_time": "2018-05-20T17:02:38.594323Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'learning_decay': 0.9, 'n_components': 2}"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Best parameters for tfidf\n",
    "model.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-23T19:00:24.223979Z",
     "start_time": "2018-05-23T19:00:19.943302Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.01837593, 0.01837593, 0.01837593, ..., 0.01837593, 0.01837593,\n",
       "        0.01837593],\n",
       "       [0.05      , 0.05      , 0.05      , ..., 0.05      , 0.05      ,\n",
       "        0.05      ],\n",
       "       [0.01830127, 0.01830127, 0.01830127, ..., 0.01830127, 0.01830127,\n",
       "        0.01830127],\n",
       "       ...,\n",
       "       [0.02071068, 0.02071068, 0.02071068, ..., 0.02071068, 0.02071068,\n",
       "        0.02071068],\n",
       "       [0.025     , 0.025     , 0.025     , ..., 0.025     , 0.525     ,\n",
       "        0.025     ],\n",
       "       [0.05      , 0.05      , 0.05      , ..., 0.05      , 0.05      ,\n",
       "        0.05      ]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Optimised LDA topic model fit based on tfidf_vectorizer \n",
    "lda_tfidf = LatentDirichletAllocation(n_components=20, random_state=0, n_jobs=-1, learning_method='online', learning_decay=0.9)\n",
    "lda_tfidf.fit_transform(X_tfidf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1.2 Visualising LDA (Latent Dirichlet Allocation) topic models\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-23T19:00:30.802848Z",
     "start_time": "2018-05-23T19:00:28.007851Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<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_el1148027357163516964343417949\"></div>\n",
       "<script type=\"text/javascript\">\n",
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huge gum\", \"better kind thoughts\", \"cat hanging upside\", \"cuddling black dog\", \"cuddling black dog\", \"darn black dog\", \"dog feral cat\", \"enjoy time family\", \"feel bit better\", \"fight tired unwell\", \"going try make\", \"got nan dog\", \"got nan dog\", \"hour drive away\", \"huge gum leaves\", \"makes lot sense\", \"mental health issues\", \"need find works\", \"oh well hope\", \"oh well hope\", \"picnic drs office\", \"please ever give\", \"positivity motivation humour\", \"sending virtual hugs\", \"sorry hear struggling\", \"thanks going try\", \"thanks much comments\", \"tired unwell tired\", \"unwell tired trying\", \"wave everyone else\"]}, \"R\": 30, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [15, 13, 6, 4, 1, 2, 5, 19, 20, 11, 14, 16, 3, 18, 10, 12, 8, 17, 9, 7]};\n",
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      "text/plain": [
       "PreparedData(topic_coordinates=            Freq  cluster  topics          x          y\n",
       "topic                                                  \n",
       "14     10.101905        1       1   7.240584  62.883759\n",
       "12      9.674921        1       2  -9.646950  46.858959\n",
       "5       9.213016        1       3   9.710749  11.567299\n",
       "3       7.468571        1       4 -24.184044  31.333546\n",
       "0       6.824127        1       5 -65.995979  24.003578\n",
       "1       6.357460        1       6 -55.838463  47.858727\n",
       "4       5.879683        1       7 -52.419456   2.790928\n",
       "18      5.635238        1       8 -29.879637  10.594707\n",
       "19      4.746349        1       9 -34.159710 -12.518440\n",
       "10      4.690794        1      10  13.395372  -9.988531\n",
       "13      4.357460        1      11  25.601421  46.565048\n",
       "15      4.079683        1      12 -43.830921  27.192461\n",
       "2       3.857460        1      13 -10.824326 -20.805817\n",
       "17      3.746349        1      14   7.080748  34.484890\n",
       "9       3.190794        1      15 -41.788685  69.481651\n",
       "11      2.635238        1      16 -31.888054  49.533173\n",
       "7       2.635238        1      17 -16.134815  69.646927\n",
       "16      1.635238        1      18 -11.162503   0.976201\n",
       "8       1.635238        1      19  -8.588459  21.183186\n",
       "6       1.635238        1      20  29.763134  20.532961, topic_info=     Category      Freq                          Term     Total  loglift  \\\n",
       "term                                                                       \n",
       "22    Default  2.000000                   got nan dog  2.000000  30.0000   \n",
       "12    Default  3.000000             enjoy time family  3.000000  29.0000   \n",
       "4     Default  2.000000            cat hanging upside  2.000000  28.0000   \n",
       "34    Default  4.000000          mental health issues  4.000000  27.0000   \n",
       "20    Default  4.000000                going try make  4.000000  26.0000   \n",
       "7     Default  3.000000            cuddling black dog  3.000000  25.0000   \n",
       "62    Default  2.000000            wave everyone else  2.000000  24.0000   \n",
       "32    Default  2.000000               makes lot sense  2.000000  23.0000   \n",
       "15    Default  2.000000               feel bit better  2.000000  22.0000   \n",
       "40    Default  3.000000                  oh well hope  3.000000  21.0000   \n",
       "11    Default  2.000000                 dog feral cat  2.000000  20.0000   \n",
       "55    Default  3.000000              thanks going try  3.000000  19.0000   \n",
       "56    Default  2.000000          thanks much comments  2.000000  18.0000   \n",
       "51    Default  2.000000          sending virtual hugs  2.000000  17.0000   \n",
       "9     Default  3.000000                darn black dog  3.000000  16.0000   \n",
       "31    Default  1.000000               love go camping  1.000000  15.0000   \n",
       "52    Default  1.000000      snarling german shepherd  1.000000  14.0000   \n",
       "28    Default  1.000000            joined craft group  1.000000  13.0000   \n",
       "36    Default  1.000000               much kind words  1.000000  12.0000   \n",
       "26    Default  2.000000               hour drive away  2.000000  11.0000   \n",
       "3     Default  1.000000                care good lady  1.000000  10.0000   \n",
       "45    Default  2.000000  positivity motivation humour  2.000000   9.0000   \n",
       "41    Default  2.000000            outside look stars  2.000000   8.0000   \n",
       "1     Default  2.000000          better kind thoughts  2.000000   7.0000   \n",
       "27    Default  2.000000               huge gum leaves  2.000000   6.0000   \n",
       "61    Default  1.000000              wanting run away  1.000000   5.0000   \n",
       "54    Default  2.000000               taken long time  2.000000   4.0000   \n",
       "49    Default  1.000000         scenery driving along  1.000000   3.0000   \n",
       "50    Default  1.000000               self help books  1.000000   2.0000   \n",
       "30    Default  2.000000            loads virtual hugs  2.000000   1.0000   \n",
       "...       ...       ...                           ...       ...      ...   \n",
       "49    Topic20  0.037324         scenery driving along  1.896792   0.1851   \n",
       "17    Topic20  0.038743      feeling really depressed  1.972940   0.1830   \n",
       "61    Topic20  0.038149              wanting run away  1.975778   0.1662   \n",
       "16    Topic20  0.039487           feel cheerful happy  2.047603   0.1649   \n",
       "48    Topic20  0.038382           said really enjoyed  1.998406   0.1609   \n",
       "27    Topic20  0.038639               huge gum leaves  2.044283   0.1449   \n",
       "30    Topic20  0.038289            loads virtual hugs  2.051280   0.1323   \n",
       "37    Topic20  0.036931         need fight depression  1.986409   0.1284   \n",
       "38    Topic20  0.036534                need find ways  1.978636   0.1215   \n",
       "5     Topic20  0.036743              channel end game  1.990763   0.1211   \n",
       "41    Topic20  0.036921            outside look stars  2.011622   0.1155   \n",
       "29    Topic20  0.037821            laughter good soul  2.072063   0.1099   \n",
       "33    Topic20  0.034675         many wonderful people  1.908912   0.1051   \n",
       "44    Topic20  0.036669              please ever give  2.053694   0.0879   \n",
       "43    Topic20  0.037466             picnic drs office  2.100978   0.0866   \n",
       "0     Topic20  0.037482                 ants huge gum  2.102645   0.0863   \n",
       "52    Topic20  0.034523      snarling german shepherd  1.950544   0.0792   \n",
       "59    Topic20  0.039398            tired unwell tired  2.233044   0.0760   \n",
       "60    Topic20  0.039916           unwell tired trying  2.276230   0.0699   \n",
       "13    Topic20  0.035272          exhausting full stop  2.020967   0.0651   \n",
       "19    Topic20  0.034350             go round emotions  1.971947   0.0632   \n",
       "20    Topic20  0.074538                going try make  4.660025  -0.0221   \n",
       "39    Topic20  0.036915               need find works  2.148677   0.0494   \n",
       "63    Topic20  0.036500     wondering time depression  2.133534   0.0452   \n",
       "32    Topic20  0.039016               makes lot sense  2.416282  -0.0126   \n",
       "4     Topic20  0.039901            cat hanging upside  2.794104  -0.1355   \n",
       "22    Topic20  0.038666                   got nan dog  2.869853  -0.1937   \n",
       "7     Topic20  0.039047            cuddling black dog  3.584160  -0.4061   \n",
       "56    Topic20  0.037090          thanks much comments  2.537081  -0.1121   \n",
       "34    Topic20  0.036979          mental health issues  4.730097  -0.7380   \n",
       "\n",
       "      logprob  \n",
       "term           \n",
       "22    30.0000  \n",
       "12    29.0000  \n",
       "4     28.0000  \n",
       "34    27.0000  \n",
       "20    26.0000  \n",
       "7     25.0000  \n",
       "62    24.0000  \n",
       "32    23.0000  \n",
       "15    22.0000  \n",
       "40    21.0000  \n",
       "11    20.0000  \n",
       "55    19.0000  \n",
       "56    18.0000  \n",
       "51    17.0000  \n",
       "9     16.0000  \n",
       "31    15.0000  \n",
       "52    14.0000  \n",
       "28    13.0000  \n",
       "36    12.0000  \n",
       "26    11.0000  \n",
       "3     10.0000  \n",
       "45     9.0000  \n",
       "41     8.0000  \n",
       "1      7.0000  \n",
       "27     6.0000  \n",
       "61     5.0000  \n",
       "54     4.0000  \n",
       "49     3.0000  \n",
       "50     2.0000  \n",
       "30     1.0000  \n",
       "...       ...  \n",
       "49    -4.1854  \n",
       "17    -4.1481  \n",
       "61    -4.1635  \n",
       "16    -4.1290  \n",
       "48    -4.1574  \n",
       "27    -4.1507  \n",
       "30    -4.1599  \n",
       "37    -4.1959  \n",
       "38    -4.2068  \n",
       "5     -4.2011  \n",
       "41    -4.1962  \n",
       "29    -4.1721  \n",
       "33    -4.2590  \n",
       "44    -4.2031  \n",
       "43    -4.1816  \n",
       "0     -4.1811  \n",
       "52    -4.2634  \n",
       "59    -4.1313  \n",
       "60    -4.1182  \n",
       "13    -4.2419  \n",
       "19    -4.2684  \n",
       "20    -3.4937  \n",
       "39    -4.1964  \n",
       "63    -4.2077  \n",
       "32    -4.1410  \n",
       "4     -4.1186  \n",
       "22    -4.1500  \n",
       "7     -4.1402  \n",
       "56    -4.1917  \n",
       "34    -4.1946  \n",
       "\n",
       "[803 rows x 6 columns], token_table=      Topic      Freq                          Term\n",
       "term                                               \n",
       "0         4  0.475592                 ants huge gum\n",
       "1         5  0.497740          better kind thoughts\n",
       "4        15  0.357897            cat hanging upside\n",
       "7        10  0.279005            cuddling black dog\n",
       "7        14  0.279005            cuddling black dog\n",
       "9         1  0.327124                darn black dog\n",
       "11       10  0.402389                 dog feral cat\n",
       "12        8  0.604494             enjoy time family\n",
       "15        7  0.370425               feel bit better\n",
       "18        2  0.446071            fight tired unwell\n",
       "20        1  0.643773                going try make\n",
       "22        2  0.348450                   got nan dog\n",
       "22       17  0.348450                   got nan dog\n",
       "26        1  0.364330               hour drive away\n",
       "27        4  0.489169               huge gum leaves\n",
       "32       14  0.413859               makes lot sense\n",
       "34        3  0.634236          mental health issues\n",
       "39        2  0.465403               need find works\n",
       "40        1  0.284714                  oh well hope\n",
       "40        7  0.284714                  oh well hope\n",
       "43        4  0.475969             picnic drs office\n",
       "44        4  0.486927              please ever give\n",
       "45        6  0.445268  positivity motivation humour\n",
       "51        4  0.370210          sending virtual hugs\n",
       "53        5  0.445837         sorry hear struggling\n",
       "55        1  0.567445              thanks going try\n",
       "56       10  0.394154          thanks much comments\n",
       "59        2  0.447819            tired unwell tired\n",
       "60        2  0.439323           unwell tired trying\n",
       "62       15  0.451196            wave everyone else, R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[15, 13, 6, 4, 1, 2, 5, 19, 20, 11, 14, 16, 3, 18, 10, 12, 8, 17, 9, 7])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualising LDA topic model based on CountVectorizer\n",
    "pyLDAvis.sklearn.prepare(lda_cvt, X_cvt, cvt_vectorizer, n_jobs=1, mds='tsne')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-23T19:09:04.193867Z",
     "start_time": "2018-05-23T19:08:56.275872Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<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_el1148027357146604245415092879\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "\n",
       "var ldavis_el1148027357146604245415092879_data = {\"mdsDat\": {\"Freq\": [9.093576693099187, 7.354536701790602, 6.97451972695798, 6.881090364066926, 6.6589553403365755, 6.4053240872530015, 5.847784496435433, 5.458965482389513, 5.454297063855912, 5.120400133958642, 4.661105891961275, 4.43665390609506, 4.353512634805283, 3.5652373045218537, 3.565237291949513, 3.4019801233615685, 3.4014746545178927, 2.613704801199201, 2.6137047785751726, 2.1379385228693972], \"cluster\": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], \"topics\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20], \"x\": [57.34080123901367, 21.855695724487305, -127.4720230102539, -7.209629535675049, 13.966713905334473, 30.86600112915039, -44.387264251708984, -51.53215026855469, -100.03620910644531, 34.5999641418457, -42.709590911865234, 67.37528991699219, -62.15419006347656, -11.369043350219727, 26.812639236450195, 41.1734733581543, 50.96506881713867, -0.5522763729095459, 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       "\n",
       "function LDAvis_load_lib(url, callback){\n",
       "  var s = document.createElement('script');\n",
       "  s.src = url;\n",
       "  s.async = true;\n",
       "  s.onreadystatechange = s.onload = callback;\n",
       "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
       "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "}\n",
       "\n",
       "if(typeof(LDAvis) !== \"undefined\"){\n",
       "   // already loaded: just create the visualization\n",
       "   !function(LDAvis){\n",
       "       new LDAvis(\"#\" + \"ldavis_el1148027357146604245415092879\", ldavis_el1148027357146604245415092879_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",
       "        new LDAvis(\"#\" + \"ldavis_el1148027357146604245415092879\", ldavis_el1148027357146604245415092879_data);\n",
       "      });\n",
       "    });\n",
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       "    // 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_el1148027357146604245415092879\", ldavis_el1148027357146604245415092879_data);\n",
       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "PreparedData(topic_coordinates=           Freq  cluster  topics           x           y\n",
       "topic                                                   \n",
       "0      9.093577        1       1   57.340801  -45.160965\n",
       "5      7.354537        1       2   21.855696  146.361694\n",
       "10     6.974520        1       3 -127.472023 -247.404434\n",
       "18     6.881090        1       4   -7.209630  -64.421181\n",
       "3      6.658955        1       5   13.966714   -0.807747\n",
       "1      6.405324        1       6   30.866001  -61.979271\n",
       "14     5.847784        1       7  -44.387264 -219.847183\n",
       "4      5.458965        1       8  -51.532150  -22.693823\n",
       "15     5.454297        1       9 -100.036209   68.530373\n",
       "12     5.120400        1      10   34.599964 -111.542145\n",
       "17     4.661106        1      11  -42.709591  -74.722687\n",
       "13     4.436654        1      12   67.375290  -77.222115\n",
       "2      4.353513        1      13  -62.154190 -197.095779\n",
       "11     3.565237        1      14  -11.369043  -31.883074\n",
       "7      3.565237        1      15   26.812639   91.626335\n",
       "9      3.401980        1      16   41.173473   -8.377916\n",
       "6      3.401475        1      17   50.965069   33.773052\n",
       "16     2.613705        1      18   -0.552276 -116.366982\n",
       "19     2.613705        1      19   76.186279  361.444550\n",
       "8      2.137939        1      20   77.538986  -20.779543, topic_info=     Category      Freq                          Term     Total  loglift  \\\n",
       "term                                                                       \n",
       "22    Default  2.000000                   got nan dog  2.000000  30.0000   \n",
       "12    Default  2.000000             enjoy time family  2.000000  29.0000   \n",
       "34    Default  2.000000          mental health issues  2.000000  28.0000   \n",
       "4     Default  2.000000            cat hanging upside  2.000000  27.0000   \n",
       "20    Default  2.000000                going try make  2.000000  26.0000   \n",
       "62    Default  1.000000            wave everyone else  1.000000  25.0000   \n",
       "55    Default  1.000000              thanks going try  1.000000  24.0000   \n",
       "40    Default  2.000000                  oh well hope  2.000000  23.0000   \n",
       "7     Default  2.000000            cuddling black dog  2.000000  22.0000   \n",
       "32    Default  1.000000               makes lot sense  1.000000  21.0000   \n",
       "15    Default  1.000000               feel bit better  1.000000  20.0000   \n",
       "9     Default  1.000000                darn black dog  1.000000  19.0000   \n",
       "11    Default  1.000000                 dog feral cat  1.000000  18.0000   \n",
       "51    Default  1.000000          sending virtual hugs  1.000000  17.0000   \n",
       "56    Default  1.000000          thanks much comments  1.000000  16.0000   \n",
       "31    Default  1.000000               love go camping  1.000000  15.0000   \n",
       "45    Default  1.000000  positivity motivation humour  1.000000  14.0000   \n",
       "26    Default  1.000000               hour drive away  1.000000  13.0000   \n",
       "61    Default  1.000000              wanting run away  1.000000  12.0000   \n",
       "52    Default  1.000000      snarling german shepherd  1.000000  11.0000   \n",
       "28    Default  1.000000            joined craft group  1.000000  10.0000   \n",
       "36    Default  1.000000               much kind words  1.000000   9.0000   \n",
       "5     Default  1.000000              channel end game  1.000000   8.0000   \n",
       "49    Default  1.000000         scenery driving along  1.000000   7.0000   \n",
       "44    Default  1.000000              please ever give  1.000000   6.0000   \n",
       "33    Default  1.000000         many wonderful people  1.000000   5.0000   \n",
       "46    Default  1.000000                   ray ct scan  1.000000   4.0000   \n",
       "54    Default  1.000000               taken long time  1.000000   3.0000   \n",
       "1     Default  1.000000          better kind thoughts  1.000000   2.0000   \n",
       "50    Default  1.000000               self help books  1.000000   1.0000   \n",
       "...       ...       ...                           ...       ...      ...   \n",
       "18    Topic20  0.036988            fight tired unwell  1.429376   0.1909   \n",
       "35    Topic20  0.036557                merry go round  1.415018   0.1893   \n",
       "16    Topic20  0.038692           feel cheerful happy  1.517417   0.1762   \n",
       "64    Topic20  0.036200       write gratitude journal  1.422270   0.1744   \n",
       "60    Topic20  0.036648           unwell tired trying  1.455415   0.1637   \n",
       "33    Topic20  0.035667         many wonderful people  1.433749   0.1515   \n",
       "39    Topic20  0.035605               need find works  1.436807   0.1476   \n",
       "25    Topic20  0.036781           hoping today better  1.500646   0.1366   \n",
       "52    Topic20  0.035011      snarling german shepherd  1.430484   0.1352   \n",
       "36    Topic20  0.035691               much kind words  1.463060   0.1319   \n",
       "59    Topic20  0.034781            tired unwell tired  1.432726   0.1271   \n",
       "24    Topic20  0.036452               hope okay today  1.513199   0.1194   \n",
       "10    Topic20  0.035464     depression explain people  1.483659   0.1116   \n",
       "23    Topic20  0.034155              help feel better  1.430735   0.1103   \n",
       "14    Topic20  0.035892         explain people around  1.503762   0.1101   \n",
       "38    Topic20  0.035233                need find ways  1.479864   0.1076   \n",
       "28    Topic20  0.033195            joined craft group  1.401658   0.1023   \n",
       "0     Topic20  0.035459                 ants huge gum  1.499073   0.1011   \n",
       "37    Topic20  0.034903         need fight depression  1.477759   0.0996   \n",
       "13    Topic20  0.035142          exhausting full stop  1.493461   0.0959   \n",
       "30    Topic20  0.035444            loads virtual hugs  1.522331   0.0853   \n",
       "54    Topic20  0.033163               taken long time  1.424769   0.0850   \n",
       "44    Topic20  0.035050              please ever give  1.509165   0.0828   \n",
       "58    Topic20  0.032212       time depression explain  1.388672   0.0816   \n",
       "46    Topic20  0.035994                   ray ct scan  1.564593   0.0733   \n",
       "26    Topic20  0.035043               hour drive away  1.547159   0.0577   \n",
       "7     Topic20  0.044115            cuddling black dog  2.379399  -0.1425   \n",
       "11    Topic20  0.038360                 dog feral cat  1.895289  -0.0548   \n",
       "62    Topic20  0.035574            wave everyone else  1.637153   0.0162   \n",
       "45    Topic20  0.034971  positivity motivation humour  1.685330  -0.0299   \n",
       "\n",
       "      logprob  \n",
       "term           \n",
       "22    30.0000  \n",
       "12    29.0000  \n",
       "34    28.0000  \n",
       "4     27.0000  \n",
       "20    26.0000  \n",
       "62    25.0000  \n",
       "55    24.0000  \n",
       "40    23.0000  \n",
       "7     22.0000  \n",
       "32    21.0000  \n",
       "15    20.0000  \n",
       "9     19.0000  \n",
       "11    18.0000  \n",
       "51    17.0000  \n",
       "56    16.0000  \n",
       "31    15.0000  \n",
       "45    14.0000  \n",
       "26    13.0000  \n",
       "61    12.0000  \n",
       "52    11.0000  \n",
       "28    10.0000  \n",
       "36     9.0000  \n",
       "5      8.0000  \n",
       "49     7.0000  \n",
       "44     6.0000  \n",
       "33     5.0000  \n",
       "46     4.0000  \n",
       "54     3.0000  \n",
       "1      2.0000  \n",
       "50     1.0000  \n",
       "...       ...  \n",
       "18    -4.1067  \n",
       "35    -4.1184  \n",
       "16    -4.0616  \n",
       "64    -4.1282  \n",
       "60    -4.1159  \n",
       "33    -4.1431  \n",
       "39    -4.1448  \n",
       "25    -4.1123  \n",
       "52    -4.1616  \n",
       "36    -4.1424  \n",
       "59    -4.1682  \n",
       "24    -4.1213  \n",
       "10    -4.1488  \n",
       "23    -4.1864  \n",
       "14    -4.1368  \n",
       "38    -4.1553  \n",
       "28    -4.2149  \n",
       "0     -4.1489  \n",
       "37    -4.1647  \n",
       "13    -4.1579  \n",
       "30    -4.1493  \n",
       "54    -4.2158  \n",
       "44    -4.1605  \n",
       "58    -4.2449  \n",
       "46    -4.1339  \n",
       "26    -4.1607  \n",
       "7     -3.9305  \n",
       "11    -4.0703  \n",
       "62    -4.1456  \n",
       "45    -4.1628  \n",
       "\n",
       "[777 rows x 6 columns], token_table=      Topic      Freq                  Term\n",
       "term                                       \n",
       "4         4  0.438914    cat hanging upside\n",
       "7         3  0.420274    cuddling black dog\n",
       "9         7  0.539957        darn black dog\n",
       "11        3  0.527624         dog feral cat\n",
       "12        4  0.382599     enjoy time family\n",
       "15        8  0.560167       feel bit better\n",
       "20        7  0.468622        going try make\n",
       "22       15  0.429783           got nan dog\n",
       "32       11  0.551614       makes lot sense\n",
       "34        2  0.367602  mental health issues\n",
       "40        7  0.458723          oh well hope\n",
       "40        8  0.458723          oh well hope\n",
       "51        5  0.521364  sending virtual hugs\n",
       "56        3  0.564630  thanks much comments, R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[1, 6, 11, 19, 4, 2, 15, 5, 16, 13, 18, 14, 3, 12, 8, 10, 7, 17, 20, 9])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Visualising LDA Topic model based on tfidf_vectorizer\n",
    "pyLDAvis.sklearn.prepare(lda_tfidf, X_tfidf, tfidf_vectorizer, n_jobs=-1, mds='tsne')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.2 Sentiment Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-23T19:11:10.875640Z",
     "start_time": "2018-05-23T19:11:08.887824Z"
    }
   },
   "outputs": [],
   "source": [
    "# import vader sentiment analyser\n",
    "from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-23T19:11:11.064649Z",
     "start_time": "2018-05-23T19:11:10.919645Z"
    }
   },
   "outputs": [],
   "source": [
    "#instantiation of sentiment analyser\n",
    "sentiment_analyzer = SentimentIntensityAnalyzer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-23T19:11:11.870183Z",
     "start_time": "2018-05-23T19:11:11.866185Z"
    }
   },
   "outputs": [],
   "source": [
    "#create empty dataframe to store sentiment scores for each post\n",
    "polarity_scores = pd.DataFrame(columns=[\"post\", \"sentiment_score\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-23T19:11:13.648235Z",
     "start_time": "2018-05-23T19:11:12.726145Z"
    }
   },
   "outputs": [],
   "source": [
    "# computing sentimment scores for each post and populating dataframe\n",
    "posts = []\n",
    "polarities = []\n",
    "for post in postings.content:\n",
    "    polarity = sentiment_analyzer.polarity_scores(post)\n",
    "    posts.append(post)\n",
    "    polarities.append(polarity)\n",
    "\n",
    "polarity_scores[\"post\"] = posts\n",
    "polarity_scores[\"sentiment_score\"] = polarities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-23T19:11:14.803343Z",
     "start_time": "2018-05-23T19:11:14.770347Z"
    }
   },
   "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>post</th>\n",
       "      <th>sentiment_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Right now I feel like I don't have the energy ...</td>\n",
       "      <td>{'neg': 0.26, 'neu': 0.544, 'pos': 0.196, 'com...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mrs dool.\\nI am sending you a big reassuring h...</td>\n",
       "      <td>{'neg': 0.158, 'neu': 0.608, 'pos': 0.234, 'co...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Hi Doolhof\\nI'm sorry that you feel so low.  Y...</td>\n",
       "      <td>{'neg': 0.127, 'neu': 0.686, 'pos': 0.187, 'co...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Hi Quirky,\\nThanks for the virtual hug, I need...</td>\n",
       "      <td>{'neg': 0.144, 'neu': 0.727, 'pos': 0.129, 'co...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Hi Summer Rose,\\nThanks for your encouragement...</td>\n",
       "      <td>{'neg': 0.156, 'neu': 0.708, 'pos': 0.136, 'co...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Dools so sorry to hear you're in deep darl.\\nI...</td>\n",
       "      <td>{'neg': 0.045, 'neu': 0.643, 'pos': 0.313, 'co...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Dear Dools how are you feeling today darl 🤗\\nI...</td>\n",
       "      <td>{'neg': 0.086, 'neu': 0.633, 'pos': 0.282, 'co...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Dear DB,\\nThank you so very much. I have been ...</td>\n",
       "      <td>{'neg': 0.086, 'neu': 0.738, 'pos': 0.176, 'co...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Hi all \\nYeah it does pull us under its so dam...</td>\n",
       "      <td>{'neg': 0.079, 'neu': 0.569, 'pos': 0.351, 'co...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Hi DB,\\nWoke up this morning wondering why I h...</td>\n",
       "      <td>{'neg': 0.199, 'neu': 0.731, 'pos': 0.07, 'com...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Ah Mrs D, so sorry you are hurting. Here is a ...</td>\n",
       "      <td>{'neg': 0.216, 'neu': 0.633, 'pos': 0.151, 'co...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Ahh Dools 🤗 how awful how you're feeling. Feel...</td>\n",
       "      <td>{'neg': 0.157, 'neu': 0.506, 'pos': 0.337, 'co...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Hi Mrs D, \\nI'm relieved you asked for help. I...</td>\n",
       "      <td>{'neg': 0.108, 'neu': 0.72, 'pos': 0.171, 'com...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Dear Nat, DB and Shell,\\nI have tears of grati...</td>\n",
       "      <td>{'neg': 0.101, 'neu': 0.766, 'pos': 0.133, 'co...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Hi Dools\\nAwwww, sending you loads of virtual ...</td>\n",
       "      <td>{'neg': 0.032, 'neu': 0.804, 'pos': 0.164, 'co...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Dear Bev\\nI would give anything to have the wo...</td>\n",
       "      <td>{'neg': 0.106, 'neu': 0.722, 'pos': 0.173, 'co...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Hi PamelaR,\\nThanks for your message. I have b...</td>\n",
       "      <td>{'neg': 0.067, 'neu': 0.727, 'pos': 0.206, 'co...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Hi Mary,\\nThanks so much for your very kind wo...</td>\n",
       "      <td>{'neg': 0.101, 'neu': 0.757, 'pos': 0.142, 'co...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Hello Dools\\nMemory caught up with me.... no i...</td>\n",
       "      <td>{'neg': 0.056, 'neu': 0.735, 'pos': 0.209, 'co...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Hi Mrs D (and a wave to everyone else of cours...</td>\n",
       "      <td>{'neg': 0.103, 'neu': 0.706, 'pos': 0.192, 'co...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>So many beautiful people 🤗\\nI imagined once a ...</td>\n",
       "      <td>{'neg': 0.106, 'neu': 0.558, 'pos': 0.336, 'co...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Hi Doolhof\\nI've been offline awhile and just ...</td>\n",
       "      <td>{'neg': 0.031, 'neu': 0.817, 'pos': 0.151, 'co...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Hello Dools\\nI really hope you have relief soo...</td>\n",
       "      <td>{'neg': 0.062, 'neu': 0.671, 'pos': 0.267, 'co...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Beautifully said Rose \\nThe stars ... oh yeah ...</td>\n",
       "      <td>{'neg': 0.0, 'neu': 0.535, 'pos': 0.465, 'comp...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Dear precious people,\\nThank you so very much ...</td>\n",
       "      <td>{'neg': 0.087, 'neu': 0.717, 'pos': 0.197, 'co...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Hell Dools,\\nYou are so loved and you have a p...</td>\n",
       "      <td>{'neg': 0.106, 'neu': 0.677, 'pos': 0.217, 'co...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Hi Mrs Dools\\nI have not much to say. So I tho...</td>\n",
       "      <td>{'neg': 0.078, 'neu': 0.74, 'pos': 0.182, 'com...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Hi Karen,\\nThank you so very much for your ver...</td>\n",
       "      <td>{'neg': 0.068, 'neu': 0.726, 'pos': 0.206, 'co...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Thankyou Mrs Dools\\nPainting?  Well a coincide...</td>\n",
       "      <td>{'neg': 0.0, 'neu': 0.799, 'pos': 0.201, 'comp...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Yeah.. the walls go up in a second. Its like t...</td>\n",
       "      <td>{'neg': 0.152, 'neu': 0.61, 'pos': 0.238, 'com...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>Fantastic Dools. What a great day! You've achi...</td>\n",
       "      <td>{'neg': 0.06, 'neu': 0.835, 'pos': 0.104, 'com...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>Hi Chloe, Karen, Pamela and Everyone,\\nThank y...</td>\n",
       "      <td>{'neg': 0.07, 'neu': 0.774, 'pos': 0.156, 'com...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>Hey Doolsy and all \\n\\r\\nFinally got here, I'v...</td>\n",
       "      <td>{'neg': 0.129, 'neu': 0.663, 'pos': 0.208, 'co...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>LOL Dools, a snarling German Shepherd doesn't ...</td>\n",
       "      <td>{'neg': 0.036, 'neu': 0.75, 'pos': 0.214, 'com...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>Hi All,\\nI enjoyed a walk in the park before t...</td>\n",
       "      <td>{'neg': 0.052, 'neu': 0.76, 'pos': 0.188, 'com...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>lol Doolsy you've got a wicked sense of humour...</td>\n",
       "      <td>{'neg': 0.035, 'neu': 0.729, 'pos': 0.235, 'co...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>I'm sitting here with tears running down my fa...</td>\n",
       "      <td>{'neg': 0.05, 'neu': 0.584, 'pos': 0.366, 'com...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>hi demonblaster, hello again Pammy, hey Dools\\...</td>\n",
       "      <td>{'neg': 0.09, 'neu': 0.621, 'pos': 0.289, 'com...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>You poor thing having that big cry although I ...</td>\n",
       "      <td>{'neg': 0.068, 'neu': 0.625, 'pos': 0.307, 'co...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>Hi Dools\\nThankyou for the super kind post :-)...</td>\n",
       "      <td>{'neg': 0.047, 'neu': 0.704, 'pos': 0.249, 'co...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>Hey Paul, Mate,\\nHI. Please know I have not ta...</td>\n",
       "      <td>{'neg': 0.071, 'neu': 0.729, 'pos': 0.2, 'comp...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>Hi Chloe, Pammy, DB, Paul and All reading,\\nTh...</td>\n",
       "      <td>{'neg': 0.046, 'neu': 0.795, 'pos': 0.159, 'co...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>Hi Dools,\\nThat lady at the Op Shop sounds lov...</td>\n",
       "      <td>{'neg': 0.121, 'neu': 0.718, 'pos': 0.16, 'com...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>Hello Dools (wave to everyone else)\\nPlease do...</td>\n",
       "      <td>{'neg': 0.034, 'neu': 0.702, 'pos': 0.263, 'co...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>Hi Chloe, PamelaR and All,\\nIt does seem to he...</td>\n",
       "      <td>{'neg': 0.042, 'neu': 0.8, 'pos': 0.158, 'comp...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>Hi Dools,\\nOnce again, your imaginative mind m...</td>\n",
       "      <td>{'neg': 0.035, 'neu': 0.805, 'pos': 0.16, 'com...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>Hello Dools, and everyone else.\\nThe upside do...</td>\n",
       "      <td>{'neg': 0.0, 'neu': 0.727, 'pos': 0.273, 'comp...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>Hi Chloe,\\nIt is a bit of a journey to get to ...</td>\n",
       "      <td>{'neg': 0.079, 'neu': 0.787, 'pos': 0.134, 'co...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>Hey Grandy,\\nThanks. I am going to try to make...</td>\n",
       "      <td>{'neg': 0.08, 'neu': 0.784, 'pos': 0.136, 'com...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>Hi Dools,\\nmaybe you could leave your cat and ...</td>\n",
       "      <td>{'neg': 0.03, 'neu': 0.815, 'pos': 0.154, 'com...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>Hi Chloe,\\nThanks. I am going to try to make p...</td>\n",
       "      <td>{'neg': 0.02, 'neu': 0.771, 'pos': 0.21, 'comp...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>Hey Dools,\\nthats good that you are going. I a...</td>\n",
       "      <td>{'neg': 0.022, 'neu': 0.721, 'pos': 0.257, 'co...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>Hello Dools,\\nIm just popping in to wish you a...</td>\n",
       "      <td>{'neg': 0.0, 'neu': 0.619, 'pos': 0.381, 'comp...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>Hey Grandy,\\nI am very relived that nana is wi...</td>\n",
       "      <td>{'neg': 0.0, 'neu': 0.736, 'pos': 0.264, 'comp...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>Hi Chloe,\\nLast night I saw the fires on the n...</td>\n",
       "      <td>{'neg': 0.0, 'neu': 0.754, 'pos': 0.246, 'comp...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>Hi Dools,\\nyes the fires are pretty bad, the w...</td>\n",
       "      <td>{'neg': 0.126, 'neu': 0.646, 'pos': 0.228, 'co...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>Hi Chloe,\\nI hope all goes well with catching ...</td>\n",
       "      <td>{'neg': 0.044, 'neu': 0.701, 'pos': 0.255, 'co...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>Hi Dools,\\nThis morning was ok. we got a lot d...</td>\n",
       "      <td>{'neg': 0.078, 'neu': 0.648, 'pos': 0.274, 'co...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>Hello Dools , Chloe,\\nDools I hope you enjoy t...</td>\n",
       "      <td>{'neg': 0.076, 'neu': 0.614, 'pos': 0.31, 'com...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>Hugs are special, especially tight ones that m...</td>\n",
       "      <td>{'neg': 0.096, 'neu': 0.695, 'pos': 0.209, 'co...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  post  \\\n",
       "0    Right now I feel like I don't have the energy ...   \n",
       "1    Mrs dool.\\nI am sending you a big reassuring h...   \n",
       "2    Hi Doolhof\\nI'm sorry that you feel so low.  Y...   \n",
       "3    Hi Quirky,\\nThanks for the virtual hug, I need...   \n",
       "4    Hi Summer Rose,\\nThanks for your encouragement...   \n",
       "5    Dools so sorry to hear you're in deep darl.\\nI...   \n",
       "6    Dear Dools how are you feeling today darl 🤗\\nI...   \n",
       "7    Dear DB,\\nThank you so very much. I have been ...   \n",
       "8    Hi all \\nYeah it does pull us under its so dam...   \n",
       "9    Hi DB,\\nWoke up this morning wondering why I h...   \n",
       "10   Ah Mrs D, so sorry you are hurting. Here is a ...   \n",
       "11   Ahh Dools 🤗 how awful how you're feeling. Feel...   \n",
       "12   Hi Mrs D, \\nI'm relieved you asked for help. I...   \n",
       "13   Dear Nat, DB and Shell,\\nI have tears of grati...   \n",
       "14   Hi Dools\\nAwwww, sending you loads of virtual ...   \n",
       "15   Dear Bev\\nI would give anything to have the wo...   \n",
       "16   Hi PamelaR,\\nThanks for your message. I have b...   \n",
       "17   Hi Mary,\\nThanks so much for your very kind wo...   \n",
       "18   Hello Dools\\nMemory caught up with me.... no i...   \n",
       "19   Hi Mrs D (and a wave to everyone else of cours...   \n",
       "20   So many beautiful people 🤗\\nI imagined once a ...   \n",
       "21   Hi Doolhof\\nI've been offline awhile and just ...   \n",
       "22   Hello Dools\\nI really hope you have relief soo...   \n",
       "23   Beautifully said Rose \\nThe stars ... oh yeah ...   \n",
       "24   Dear precious people,\\nThank you so very much ...   \n",
       "25   Hell Dools,\\nYou are so loved and you have a p...   \n",
       "26   Hi Mrs Dools\\nI have not much to say. So I tho...   \n",
       "27   Hi Karen,\\nThank you so very much for your ver...   \n",
       "28   Thankyou Mrs Dools\\nPainting?  Well a coincide...   \n",
       "29   Yeah.. the walls go up in a second. Its like t...   \n",
       "..                                                 ...   \n",
       "120  Fantastic Dools. What a great day! You've achi...   \n",
       "121  Hi Chloe, Karen, Pamela and Everyone,\\nThank y...   \n",
       "122  Hey Doolsy and all \\n\\r\\nFinally got here, I'v...   \n",
       "123  LOL Dools, a snarling German Shepherd doesn't ...   \n",
       "124  Hi All,\\nI enjoyed a walk in the park before t...   \n",
       "125  lol Doolsy you've got a wicked sense of humour...   \n",
       "126  I'm sitting here with tears running down my fa...   \n",
       "127  hi demonblaster, hello again Pammy, hey Dools\\...   \n",
       "128  You poor thing having that big cry although I ...   \n",
       "129  Hi Dools\\nThankyou for the super kind post :-)...   \n",
       "130  Hey Paul, Mate,\\nHI. Please know I have not ta...   \n",
       "131  Hi Chloe, Pammy, DB, Paul and All reading,\\nTh...   \n",
       "132  Hi Dools,\\nThat lady at the Op Shop sounds lov...   \n",
       "133  Hello Dools (wave to everyone else)\\nPlease do...   \n",
       "134  Hi Chloe, PamelaR and All,\\nIt does seem to he...   \n",
       "135  Hi Dools,\\nOnce again, your imaginative mind m...   \n",
       "136  Hello Dools, and everyone else.\\nThe upside do...   \n",
       "137  Hi Chloe,\\nIt is a bit of a journey to get to ...   \n",
       "138  Hey Grandy,\\nThanks. I am going to try to make...   \n",
       "139  Hi Dools,\\nmaybe you could leave your cat and ...   \n",
       "140  Hi Chloe,\\nThanks. I am going to try to make p...   \n",
       "141  Hey Dools,\\nthats good that you are going. I a...   \n",
       "142  Hello Dools,\\nIm just popping in to wish you a...   \n",
       "143  Hey Grandy,\\nI am very relived that nana is wi...   \n",
       "144  Hi Chloe,\\nLast night I saw the fires on the n...   \n",
       "145  Hi Dools,\\nyes the fires are pretty bad, the w...   \n",
       "146  Hi Chloe,\\nI hope all goes well with catching ...   \n",
       "147  Hi Dools,\\nThis morning was ok. we got a lot d...   \n",
       "148  Hello Dools , Chloe,\\nDools I hope you enjoy t...   \n",
       "149  Hugs are special, especially tight ones that m...   \n",
       "\n",
       "                                       sentiment_score  \n",
       "0    {'neg': 0.26, 'neu': 0.544, 'pos': 0.196, 'com...  \n",
       "1    {'neg': 0.158, 'neu': 0.608, 'pos': 0.234, 'co...  \n",
       "2    {'neg': 0.127, 'neu': 0.686, 'pos': 0.187, 'co...  \n",
       "3    {'neg': 0.144, 'neu': 0.727, 'pos': 0.129, 'co...  \n",
       "4    {'neg': 0.156, 'neu': 0.708, 'pos': 0.136, 'co...  \n",
       "5    {'neg': 0.045, 'neu': 0.643, 'pos': 0.313, 'co...  \n",
       "6    {'neg': 0.086, 'neu': 0.633, 'pos': 0.282, 'co...  \n",
       "7    {'neg': 0.086, 'neu': 0.738, 'pos': 0.176, 'co...  \n",
       "8    {'neg': 0.079, 'neu': 0.569, 'pos': 0.351, 'co...  \n",
       "9    {'neg': 0.199, 'neu': 0.731, 'pos': 0.07, 'com...  \n",
       "10   {'neg': 0.216, 'neu': 0.633, 'pos': 0.151, 'co...  \n",
       "11   {'neg': 0.157, 'neu': 0.506, 'pos': 0.337, 'co...  \n",
       "12   {'neg': 0.108, 'neu': 0.72, 'pos': 0.171, 'com...  \n",
       "13   {'neg': 0.101, 'neu': 0.766, 'pos': 0.133, 'co...  \n",
       "14   {'neg': 0.032, 'neu': 0.804, 'pos': 0.164, 'co...  \n",
       "15   {'neg': 0.106, 'neu': 0.722, 'pos': 0.173, 'co...  \n",
       "16   {'neg': 0.067, 'neu': 0.727, 'pos': 0.206, 'co...  \n",
       "17   {'neg': 0.101, 'neu': 0.757, 'pos': 0.142, 'co...  \n",
       "18   {'neg': 0.056, 'neu': 0.735, 'pos': 0.209, 'co...  \n",
       "19   {'neg': 0.103, 'neu': 0.706, 'pos': 0.192, 'co...  \n",
       "20   {'neg': 0.106, 'neu': 0.558, 'pos': 0.336, 'co...  \n",
       "21   {'neg': 0.031, 'neu': 0.817, 'pos': 0.151, 'co...  \n",
       "22   {'neg': 0.062, 'neu': 0.671, 'pos': 0.267, 'co...  \n",
       "23   {'neg': 0.0, 'neu': 0.535, 'pos': 0.465, 'comp...  \n",
       "24   {'neg': 0.087, 'neu': 0.717, 'pos': 0.197, 'co...  \n",
       "25   {'neg': 0.106, 'neu': 0.677, 'pos': 0.217, 'co...  \n",
       "26   {'neg': 0.078, 'neu': 0.74, 'pos': 0.182, 'com...  \n",
       "27   {'neg': 0.068, 'neu': 0.726, 'pos': 0.206, 'co...  \n",
       "28   {'neg': 0.0, 'neu': 0.799, 'pos': 0.201, 'comp...  \n",
       "29   {'neg': 0.152, 'neu': 0.61, 'pos': 0.238, 'com...  \n",
       "..                                                 ...  \n",
       "120  {'neg': 0.06, 'neu': 0.835, 'pos': 0.104, 'com...  \n",
       "121  {'neg': 0.07, 'neu': 0.774, 'pos': 0.156, 'com...  \n",
       "122  {'neg': 0.129, 'neu': 0.663, 'pos': 0.208, 'co...  \n",
       "123  {'neg': 0.036, 'neu': 0.75, 'pos': 0.214, 'com...  \n",
       "124  {'neg': 0.052, 'neu': 0.76, 'pos': 0.188, 'com...  \n",
       "125  {'neg': 0.035, 'neu': 0.729, 'pos': 0.235, 'co...  \n",
       "126  {'neg': 0.05, 'neu': 0.584, 'pos': 0.366, 'com...  \n",
       "127  {'neg': 0.09, 'neu': 0.621, 'pos': 0.289, 'com...  \n",
       "128  {'neg': 0.068, 'neu': 0.625, 'pos': 0.307, 'co...  \n",
       "129  {'neg': 0.047, 'neu': 0.704, 'pos': 0.249, 'co...  \n",
       "130  {'neg': 0.071, 'neu': 0.729, 'pos': 0.2, 'comp...  \n",
       "131  {'neg': 0.046, 'neu': 0.795, 'pos': 0.159, 'co...  \n",
       "132  {'neg': 0.121, 'neu': 0.718, 'pos': 0.16, 'com...  \n",
       "133  {'neg': 0.034, 'neu': 0.702, 'pos': 0.263, 'co...  \n",
       "134  {'neg': 0.042, 'neu': 0.8, 'pos': 0.158, 'comp...  \n",
       "135  {'neg': 0.035, 'neu': 0.805, 'pos': 0.16, 'com...  \n",
       "136  {'neg': 0.0, 'neu': 0.727, 'pos': 0.273, 'comp...  \n",
       "137  {'neg': 0.079, 'neu': 0.787, 'pos': 0.134, 'co...  \n",
       "138  {'neg': 0.08, 'neu': 0.784, 'pos': 0.136, 'com...  \n",
       "139  {'neg': 0.03, 'neu': 0.815, 'pos': 0.154, 'com...  \n",
       "140  {'neg': 0.02, 'neu': 0.771, 'pos': 0.21, 'comp...  \n",
       "141  {'neg': 0.022, 'neu': 0.721, 'pos': 0.257, 'co...  \n",
       "142  {'neg': 0.0, 'neu': 0.619, 'pos': 0.381, 'comp...  \n",
       "143  {'neg': 0.0, 'neu': 0.736, 'pos': 0.264, 'comp...  \n",
       "144  {'neg': 0.0, 'neu': 0.754, 'pos': 0.246, 'comp...  \n",
       "145  {'neg': 0.126, 'neu': 0.646, 'pos': 0.228, 'co...  \n",
       "146  {'neg': 0.044, 'neu': 0.701, 'pos': 0.255, 'co...  \n",
       "147  {'neg': 0.078, 'neu': 0.648, 'pos': 0.274, 'co...  \n",
       "148  {'neg': 0.076, 'neu': 0.614, 'pos': 0.31, 'com...  \n",
       "149  {'neg': 0.096, 'neu': 0.695, 'pos': 0.209, 'co...  \n",
       "\n",
       "[150 rows x 2 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "polarity_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-23T19:11:18.285980Z",
     "start_time": "2018-05-23T19:11:18.277980Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150 entries, 0 to 149\n",
      "Data columns (total 2 columns):\n",
      "post               150 non-null object\n",
      "sentiment_score    150 non-null object\n",
      "dtypes: object(2)\n",
      "memory usage: 2.4+ KB\n"
     ]
    }
   ],
   "source": [
    "polarity_scores.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<u><b>Manual evaluation of Vader sentiment analysis by random sampling of 2 positive and 2 neutral posts. negative accuracy confirmed using proof by contradiction</b></u>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2 positive posts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-23T19:11:22.551982Z",
     "start_time": "2018-05-23T19:11:22.547014Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"Hi all \\nYeah it does pull us under its so damned powerful. I did that this last down, was heavy but not like they mostly were. Went with it. Easy to slide without resistance though\\n Yeah agree there's something in talking to it, liking your wording Dools.. talking to it, challenging thoughts I've been saying but yours is better doesnt sound like effort where challenging does. Will think of you when I word it the same ☺ thanks \\nIm not saying acceptance doesnt work ..just I don't get it yet.. may not.. does sound easier. Maybe going with it is part of acceptance ..dunno \\nI think it's a good release glad you're talking Dools\\nHope you have a restful secure sleep  🤗..eveyone..\\nnigh nite 😊\""
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "polarity_scores.post[8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-23T19:11:25.892312Z",
     "start_time": "2018-05-23T19:11:25.886310Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'compound': 0.9935, 'neg': 0.079, 'neu': 0.569, 'pos': 0.351}"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "polarity_scores.sentiment_score[8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-23T19:11:33.465013Z",
     "start_time": "2018-05-23T19:11:33.460011Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"Beautifully said Rose \\nThe stars ... oh yeah ..\\nWe're part of what we see\\nThe beauty. Intrigue. Love em\""
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "polarity_scores.post[23]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-23T19:11:34.637606Z",
     "start_time": "2018-05-23T19:11:34.632639Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'compound': 0.9313, 'neg': 0.0, 'neu': 0.535, 'pos': 0.465}"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "polarity_scores.sentiment_score[23]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2 neutral posts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-23T19:11:39.133644Z",
     "start_time": "2018-05-23T19:11:39.129676Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"Hi Dools,\\nThat lady at the Op Shop sounds lovely! Its great that you have people that you can openly talk to. It certainly helps.  \\nI try to think of my depression as a feral cat and my anxiety as a vicious dog. LOL. \\nHow's everyone feeling today? My cat is still sleeping but my dog is awake and I can feel it breathing down my neck :(\\nChloe\""
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "polarity_scores.post[132]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-23T19:11:39.600127Z",
     "start_time": "2018-05-23T19:11:39.595124Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'compound': 0.2402, 'neg': 0.121, 'neu': 0.718, 'pos': 0.16}"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "polarity_scores.sentiment_score[132]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-23T19:11:40.132124Z",
     "start_time": "2018-05-23T19:11:40.126124Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"Hugs are special, especially tight ones that make you feel like the person hugging you never wants to let you go. I'm going to try and see if I can get him to meet me at the shops tomorrow I really need to talk to someone face to face that I know will listen and not judge (and give me hugs lol). \\nDepression todsy is one of the worst days I've had... Feeling very sad and suicidal. Even though I had a good laugh this morning I am still very bad today... Dumb dog and cat getting the better of me again. I just want to go to sleep but I won't be able too .\\nHope you all had lovely days, it might cheer me up to hear that you guys did \\nchloe x\""
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "polarity_scores.post[149]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-23T19:11:42.839209Z",
     "start_time": "2018-05-23T19:11:42.833245Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'compound': 0.9342, 'neg': 0.096, 'neu': 0.695, 'pos': 0.209}"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "polarity_scores.sentiment_score[149]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-23T19:11:43.956389Z",
     "start_time": "2018-05-23T19:11:43.943427Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Negative score: 9.863999999999995\n",
      "Neutral score: 105.87299999999998\n",
      "Positive score: 34.261\n",
      "Compound score: 121.31860000000003\n"
     ]
    }
   ],
   "source": [
    "#Cumulative sentiment score for thread of 150 posts\n",
    "negative = 0.0\n",
    "neutral = 0.0\n",
    "positive = 0.0\n",
    "compound = 0.0\n",
    "for index, scores in enumerate(polarity_scores.sentiment_score):\n",
    "    negative += scores[\"neg\"]\n",
    "    neutral += scores[\"neu\"]\n",
    "    positive += scores[\"pos\"]\n",
    "    compound += scores[\"compound\"]\n",
    "\n",
    "print(\"Negative score: \" + str(negative))\n",
    "print(\"Neutral score: \" + str(neutral))\n",
    "print(\"Positive score: \" + str(positive))\n",
    "print(\"Compound score: \" + str(compound))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4 Summary of Analysis:\n",
    "### Model Selection and Implementation\n",
    "\n",
    "For the first phase, we selected a 2 vectorized models and optimised them using gridsearch. For topic modelling, LDA was used as a simple model due to its high interpretability to get an initial high-level understanding of the significant terms and themes in the thread. Based on the features of Vader sentiment analysis library in its ability to handle emoticons and textual data that has not undergone significant text normalisation (see feature listing https://github.com/cjhutto/vaderSentiment), we chose Vader for sentiment analysis.\n",
    "\n",
    "### Implementation and Evaluation\n",
    "\n",
    "Overall, in this capstone:\n",
    "\n",
    "<b>Scraping</b>\n",
    "* Scraped 1 thread in beyondblue depression forum using BeautifulSoup\n",
    "* Whilst scraping, used regex to extract the elements without html tags to save time in text preprocessing. Status codes were printed to monitor status of scraping\n",
    "* Scraped dataframe was converted to excel for persistency and use in EDA and modelling\n",
    "\n",
    "<b>EDA and modelling</b>\n",
    "* Visually examined the data and added stopwords to the standard nltk stopword list.\n",
    "* used both countvectorizer and tfidf vectorizer to preprocess and transform the text into word vectors.\n",
    "* Optimised the vectorizers using grid-search cross validation\n",
    "* Fit LDA topic model to both vectorizers and compared them\n",
    "* Separately performed sentiment analysis using Vader on the individual posts without text preprocessing\n",
    "* In conducting sentiment analysis, we computed an aggregate score of the sentiment of each posts to see the overall sentiment of the forum thread.\n",
    "* To check accuracy of sentiment analysis, a random sample of 2 neutral and 2 positive posts were examined to match human labelling vs algorithmic output\n",
    "\n",
    "\n",
    "### Inference\n",
    "\n",
    "From the topic models, we are able to make inferences about the commonly used phrases in the forum thread. Also, using sentiment analysis, we inferred that the overall sentiment of the forum thread was mainly neutral and positive. There was hardly any negativity in the forum thread which is expected in a forum of this kind. Positive reinforcement, active listening and constructive suggestions were present in this forum."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5 Next Steps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Phase 2: Increase dataset size and rerun tasks in phase 1 with improvements such as pickling the scraped dataframe instead of excel to minimise data loss\n",
    "* Phase 3: Introduce subject matter experts and continue to fine-tune models. Increase dataset to have enough data to create different recurrent neural networks such as LSTM and GRUs that handle vanishing/exploding gradient well.\n",
    "* Phase 4: Refine model for deployment to incorporate it into the chatbot\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# APPENDIX"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scripts for future use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-21T07:06:48.976145Z",
     "start_time": "2018-05-21T07:06:48.971146Z"
    }
   },
   "outputs": [],
   "source": [
    "# Sklearn imports both text preprocessing and sklearn's NLP models\n",
    "from sklearn.decomposition import TruncatedSVD # SVD singular value decomposition\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.naive_bayes import MultinomialNB # mulitinomial Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-21T07:06:51.854143Z",
     "start_time": "2018-05-21T07:06:51.850142Z"
    }
   },
   "outputs": [],
   "source": [
    "Krunal = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-21T07:17:10.095146Z",
     "start_time": "2018-05-21T07:17:10.088140Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['penalty',\n",
       " 'dual',\n",
       " 'tol',\n",
       " 'C',\n",
       " 'fit_intercept',\n",
       " 'intercept_scaling',\n",
       " 'class_weight',\n",
       " 'random_state',\n",
       " 'solver',\n",
       " 'max_iter',\n",
       " 'multi_class',\n",
       " 'verbose',\n",
       " 'warm_start',\n",
       " 'n_jobs',\n",
       " '__module__',\n",
       " '__doc__',\n",
       " '__init__',\n",
       " 'fit',\n",
       " 'predict_proba',\n",
       " 'predict_log_proba',\n",
       " '_get_param_names',\n",
       " 'get_params',\n",
       " 'set_params',\n",
       " '__repr__',\n",
       " '__getstate__',\n",
       " '__setstate__',\n",
       " '__dict__',\n",
       " '__weakref__',\n",
       " '__hash__',\n",
       " '__str__',\n",
       " '__getattribute__',\n",
       " '__setattr__',\n",
       " '__delattr__',\n",
       " '__lt__',\n",
       " '__le__',\n",
       " '__eq__',\n",
       " '__ne__',\n",
       " '__gt__',\n",
       " '__ge__',\n",
       " '__new__',\n",
       " '__reduce_ex__',\n",
       " '__reduce__',\n",
       " '__subclasshook__',\n",
       " '__init_subclass__',\n",
       " '__format__',\n",
       " '__sizeof__',\n",
       " '__dir__',\n",
       " '__class__',\n",
       " 'decision_function',\n",
       " 'predict',\n",
       " '_predict_proba_lr',\n",
       " '_estimator_type',\n",
       " 'score',\n",
       " 'densify',\n",
       " 'sparsify']"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Krunal.__dir__()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "#NLP normalisation functions - keeping these functions here in case I need it later\n",
    "\n",
    "def replace_contractions(text):\n",
    "    \"\"\"Replace contractions in string of text\"\"\"\n",
    "    return contractions.fix(text)\n",
    "\n",
    "def lemmatize_verbs(words):\n",
    "    \"\"\"Lemmatize verbs in list of tokenized words\"\"\"\n",
    "    lemmatizer = WordNetLemmatizer()\n",
    "    lemmas = []\n",
    "    for word in words:\n",
    "        lemma = lemmatizer.lemmatize(word, pos='v')\n",
    "        lemmas.append(lemma)\n",
    "    return lemmas\n",
    "\n",
    "def normalize(words):\n",
    "    \n",
    "    lemmatize_verbs(words)\n",
    "    remove_stopwords(words)\n",
    "    \n",
    "    return words\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Positive accuracy = 100.0% via 137 samples\n",
      "Negative accuracy = 9.090909090909092% via 143 samples\n"
     ]
    }
   ],
   "source": [
    "#Textblob metric sentiment analysis evaluation\n",
    "from textblob import TextBlob\n",
    "\n",
    "neg_threshold = 0.514\n",
    "pos_threshold = 0.01\n",
    "\n",
    "\n",
    "pos_count = 0\n",
    "pos_correct = 0\n",
    "\n",
    "for line in postings.content:\n",
    "    analysis = TextBlob(line)\n",
    "\n",
    "    if analysis.sentiment.polarity >= pos_threshold:\n",
    "        if analysis.sentiment.polarity > 0:\n",
    "            pos_correct += 1\n",
    "        pos_count +=1\n",
    "\n",
    "\n",
    "neg_count = 0\n",
    "neg_correct = 0\n",
    "\n",
    "for line in postings.content:\n",
    "    analysis = TextBlob(line)\n",
    "    if analysis.sentiment.polarity < neg_threshold:\n",
    "        if analysis.sentiment.polarity <= 0:\n",
    "            neg_correct += 1\n",
    "        neg_count +=1\n",
    "\n",
    "print(\"Positive accuracy = {}% via {} samples\".format(pos_correct/pos_count*100.0, pos_count))\n",
    "print(\"Negative accuracy = {}% via {} samples\".format(neg_correct/neg_count*100.0, neg_count))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Positive accuracy = 92.66666666666666% via 150 samples\n",
      "Negative accuracy = 7.333333333333333% via 150 samples\n"
     ]
    }
   ],
   "source": [
    "#Vader metric evaluation\n",
    "neg_threshold = 0.514\n",
    "pos_threshold = 0.26\n",
    "pos_count = 0\n",
    "pos_correct = 0\n",
    "\n",
    "for line in postings.content:\n",
    "    polarity = sentiment_analyzer.polarity_scores(line)\n",
    "    if not polarity['neg'] > pos_threshold:\n",
    "        if polarity['pos']-polarity['neg'] > 0:\n",
    "            pos_correct += 1\n",
    "        pos_count +=1\n",
    "\n",
    "\n",
    "neg_count = 0\n",
    "neg_correct = 0\n",
    "\n",
    "for line in postings.content:\n",
    "    polarity = sentiment_analyzer.polarity_scores(line)\n",
    "    if not polarity['pos'] > neg_threshold:\n",
    "        if polarity['pos']-polarity['neg'] <= 0:\n",
    "            neg_correct += 1\n",
    "        neg_count +=1\n",
    "\n",
    "print(\"Positive accuracy = {}% via {} samples\".format(pos_correct/pos_count*100.0, pos_count))\n",
    "print(\"Negative accuracy = {}% via {} samples\".format(neg_correct/neg_count*100.0, neg_count))"
   ]
  }
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