Topic Modelling, Dominant Topic Analysis & Visualization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing the Required Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import re\n",
    "import sys\n",
    "import matplotlib.pyplot as plt\n",
    "import nltk\n",
    "import gensim\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "from gensim import corpora\n",
    "from nltk.tokenize import TweetTokenizer\n",
    "from wordcloud import WordCloud\n",
    "from string import punctuation\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.probability import FreqDist\n",
    "from nltk.tokenize import word_tokenize\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "from gensim.models.ldamodel import LdaModel\n",
    "import pyLDAvis.gensim\n",
    "from gensim.models.coherencemodel import CoherenceModel\n",
    "import matplotlib.colors as mcolors\n",
    "from nltk.sentiment.vader import SentimentIntensityAnalyzer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "tweets = pd.read_csv('vaccination_tweets.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Cleaning Tweets\n",
    "stop_nltk= stopwords.words(\"english\")\n",
    "stop_updated= stop_nltk+[\"https\",'t.co','...']\n",
    "lemm= WordNetLemmatizer()\n",
    "tweet_tok=  TweetTokenizer()\n",
    "\n",
    "def clean_text(text):\n",
    "    tokens= tweet_tok.tokenize(text.lower())\n",
    "    tokens1 = [re.sub(r'^https://t.co/[\\w]+','',tok) for tok in tokens]\n",
    "    tokens2 = [re.sub(r'^@[\\w]+','',tok) for tok in tokens1]\n",
    "    tokens3 = [re.sub(r'^#[\\w]+','',tok) for tok in tokens2]\n",
    "    tokens4 = [re.sub(r'[0-9]+','',tok) for tok in tokens3]\n",
    "    lemmed=[lemm.lemmatize(term) for term in tokens4 if term not in  stop_updated and term not in list(punctuation) and len(term)>2]\n",
    "    res=' '.join(lemmed)\n",
    "    return res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "tweets['Clean_Tweets']= tweets['text'].apply(clean_text)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Topic Modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0,\n",
       "  '0.026*\"vaccine\" + 0.018*\"dose\" + 0.010*\"second\" + 0.009*\"arm\" + 0.009*\"side\" + 0.009*\"effect\" + 0.007*\"first\" + 0.007*\"hour\" + 0.007*\"day\" + 0.006*\"get\" + 0.006*\"health\" + 0.006*\"vaccinated\" + 0.006*\"one\" + 0.005*\"said\" + 0.005*\"emergency\" + 0.005*\"got\" + 0.005*\"post\" + 0.005*\"vaccination\" + 0.005*\"covid\" + 0.004*\"given\"'),\n",
       " (1,\n",
       "  '0.024*\"vaccine\" + 0.013*\"new\" + 0.012*\"ban\" + 0.011*\"khamenei\" + 0.010*\"announced\" + 0.010*\"work\" + 0.008*\"red\" + 0.008*\"say\" + 0.008*\"crescent\" + 0.008*\"covid\" + 0.007*\"death\" + 0.007*\"year\" + 0.007*\"take\" + 0.006*\"import\" + 0.006*\"scientist\" + 0.006*\"vaccination\" + 0.005*\"effective\" + 0.005*\"people\" + 0.005*\"science\" + 0.004*\"variant\"'),\n",
       " (2,\n",
       "  '0.073*\"vaccine\" + 0.028*\"covid\" + 0.025*\"first\" + 0.018*\"today\" + 0.017*\"pfizer\" + 0.016*\"dose\" + 0.011*\"dos\" + 0.010*\"received\" + 0.010*\"got\" + 0.009*\"get\" + 0.009*\"day\" + 0.007*\"people\" + 0.007*\"biontech\" + 0.007*\"use\" + 0.007*\"thank\" + 0.006*\"approved\" + 0.006*\"receive\" + 0.006*\"grateful\" + 0.006*\"pfizer-biontech\" + 0.005*\"vaccination\"')]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_txt = [text.split() for text in tweets['Clean_Tweets'].to_list()]\n",
    "\n",
    "# Creating the term dictionary of corpus\n",
    "dictionary = corpora.Dictionary(clean_txt)\n",
    "\n",
    "#Creating DTM\n",
    "doc_term_matrix = [dictionary.doc2bow(doc) for doc in clean_txt]\n",
    "\n",
    "# Running and Trainign LDA model on the document term matrix.\n",
    "ldamodel = LdaModel(doc_term_matrix, num_topics=3, id2word = dictionary,\n",
    "                    passes=50, \n",
    "                    random_state=42)\n",
    "ldamodel.print_topics(num_words=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_lda_topics(model, num_topics):\n",
    "    word_dict = {};\n",
    "    for i in range(num_topics):\n",
    "        words = model.show_topic(i, topn = 10);\n",
    "        word_dict['Topic # ' + '{:02d}'.format(i+1)] = [i[0] for i in words];\n",
    "    return pd.DataFrame(word_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
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       "    .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>Topic # 01</th>\n",
       "      <th>Topic # 02</th>\n",
       "      <th>Topic # 03</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>vaccine</td>\n",
       "      <td>vaccine</td>\n",
       "      <td>vaccine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>dose</td>\n",
       "      <td>new</td>\n",
       "      <td>covid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>second</td>\n",
       "      <td>ban</td>\n",
       "      <td>first</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>arm</td>\n",
       "      <td>khamenei</td>\n",
       "      <td>today</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>side</td>\n",
       "      <td>announced</td>\n",
       "      <td>pfizer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>effect</td>\n",
       "      <td>work</td>\n",
       "      <td>dose</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>first</td>\n",
       "      <td>red</td>\n",
       "      <td>dos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>hour</td>\n",
       "      <td>say</td>\n",
       "      <td>received</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>day</td>\n",
       "      <td>crescent</td>\n",
       "      <td>got</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>get</td>\n",
       "      <td>covid</td>\n",
       "      <td>get</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Topic # 01 Topic # 02 Topic # 03\n",
       "0    vaccine    vaccine    vaccine\n",
       "1       dose        new      covid\n",
       "2     second        ban      first\n",
       "3        arm   khamenei      today\n",
       "4       side  announced     pfizer\n",
       "5     effect       work       dose\n",
       "6      first        red        dos\n",
       "7       hour        say   received\n",
       "8        day   crescent        got\n",
       "9        get      covid        get"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting Top 10 words in each Document\n",
    "get_lda_topics(ldamodel, 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizaing the Topic clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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_el238821533791094081693627827\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "\n",
       "var ldavis_el238821533791094081693627827_data = {\"mdsDat\": {\"x\": [0.13048517744920476, 0.04519948558946303, -0.17568466303866775], \"y\": [0.11075772458683887, -0.15352244640534368, 0.04276472181850483], \"topics\": [1, 2, 3], \"cluster\": [1, 1, 1], \"Freq\": [47.818809509277344, 26.762447357177734, 25.41874122619629]}, \"tinfo\": {\"Category\": [\"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", 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\"second\", \"second\", \"shipment\", \"shot\", \"shot\", \"shot\", \"side\", \"site\", \"sore\", \"soreness\", \"start\", \"take\", \"take\", \"team\", \"team\", \"tell\", \"thank\", \"thank\", \"thankful\", \"thanks\", \"thanks\", \"thi\", \"thing\", \"think\", \"three\", \"time\", \"time\", \"time\", \"today\", \"toll\", \"trust\", \"trust\", \"tunnel\", \"use\", \"use\", \"used\", \"vacc\", \"vaccinated\", \"vaccinated\", \"vaccination\", \"vaccination\", \"vaccination\", \"vaccine\", \"vaccine\", \"vaccine\", \"validation\", \"validation\", \"variant\", \"via\", \"view\", \"vulnerable\", \"wait\", \"way\", \"way\", \"week\", \"week\", \"week\", \"within\", \"within\", \"work\", \"work\", \"worker\", \"year\", \"year\", \"yesterday\", \"yesterday\"]}, \"R\": 30, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [3, 1, 2]};\n",
       "\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_el238821533791094081693627827\", ldavis_el238821533791094081693627827_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_el238821533791094081693627827\", ldavis_el238821533791094081693627827_data);\n",
       "      });\n",
       "    });\n",
       "}else{\n",
       "    // require.js not available: dynamically load d3 & LDAvis\n",
       "    LDAvis_load_lib(\"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min.js\", function(){\n",
       "         LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
       "                 new LDAvis(\"#\" + \"ldavis_el238821533791094081693627827\", ldavis_el238821533791094081693627827_data);\n",
       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "PreparedData(topic_coordinates=              x         y  topics  cluster       Freq\n",
       "topic                                                \n",
       "2      0.130485  0.110758       1        1  47.818810\n",
       "0      0.045199 -0.153522       2        1  26.762447\n",
       "1     -0.175685  0.042765       3        1  25.418741, topic_info=     Category        Freq         Term       Total  loglift  logprob\n",
       "388   Default  194.000000        today  194.000000  30.0000  30.0000\n",
       "63    Default  319.000000        first  319.000000  29.0000  29.0000\n",
       "219   Default  282.000000         dose  282.000000  28.0000  28.0000\n",
       "3832  Default   67.000000          ban   67.000000  27.0000  27.0000\n",
       "3835  Default   65.000000     khamenei   65.000000  26.0000  26.0000\n",
       "...       ...         ...          ...         ...      ...      ...\n",
       "66     Topic3   28.689960       people  113.512253  -0.0057  -5.3051\n",
       "144    Topic3   24.026718         week   84.114441   0.1167  -5.4825\n",
       "428    Topic3   19.537302          way   35.702507   0.7668  -5.6893\n",
       "118    Topic3   20.612692          get  161.730026  -0.6903  -5.6358\n",
       "101    Topic3   19.299417  coronavirus   73.204750   0.0365  -5.7016\n",
       "\n",
       "[188 rows x 6 columns], token_table=      Topic      Freq         Term\n",
       "term                              \n",
       "2377      2  0.954581         ache\n",
       "412       3  0.982535    announced\n",
       "2803      3  0.938491      appears\n",
       "2072      2  0.973574  appointment\n",
       "221       1  0.995043     approved\n",
       "...     ...       ...          ...\n",
       "292       1  0.980873       worker\n",
       "17        1  0.294280         year\n",
       "17        3  0.703003         year\n",
       "820       1  0.405062    yesterday\n",
       "820       2  0.582276    yesterday\n",
       "\n",
       "[213 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[3, 1, 2])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pyLDAvis.enable_notebook()\n",
    "vis = pyLDAvis.gensim.prepare(ldamodel, doc_term_matrix, dictionary=ldamodel.id2word)\n",
    "vis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculating Perplexity & Coherence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Perplexity:  -7.519221526609267\n",
      "Coherence Score:  0.3119756573020392\n"
     ]
    }
   ],
   "source": [
    "# Compute Perplexity\n",
    "print('Perplexity: ', ldamodel.log_perplexity(doc_term_matrix))\n",
    "\n",
    "# Compute Coherence\n",
    "coherence_model_lda = CoherenceModel(model=ldamodel, texts=clean_txt, dictionary=ldamodel.id2word, coherence='c_v')\n",
    "with np.errstate(invalid='ignore'):\n",
    "    lda_score = coherence_model_lda.get_coherence()\n",
    "print('Coherence Score: ', lda_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dominant Topic Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Document_No</th>\n",
       "      <th>Dominant_Topic</th>\n",
       "      <th>Topic_Perc_Contrib</th>\n",
       "      <th>Keywords</th>\n",
       "      <th>Text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.9197</td>\n",
       "      <td>vaccine, dose, second, arm, side, effect, firs...</td>\n",
       "      <td>[folk, said, daikon, paste, could, treat, cyto...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.6308</td>\n",
       "      <td>vaccine, new, ban, khamenei, announced, work, ...</td>\n",
       "      <td>[world, wrong, side, history, year, hopefully,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.8509</td>\n",
       "      <td>vaccine, covid, first, today, pfizer, dose, do...</td>\n",
       "      <td>[russian, vaccine, created, last, year]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.9314</td>\n",
       "      <td>vaccine, dose, second, arm, side, effect, firs...</td>\n",
       "      <td>[fact, immutable, senator, even, ethically, st...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.5936</td>\n",
       "      <td>vaccine, covid, first, today, pfizer, dose, do...</td>\n",
       "      <td>[explain, need, vaccine]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.9242</td>\n",
       "      <td>vaccine, dose, second, arm, side, effect, firs...</td>\n",
       "      <td>[anyone, useful, advice, guidance, whether, co...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5541</td>\n",
       "      <td>vaccine, dose, second, arm, side, effect, firs...</td>\n",
       "      <td>[bit, sad, claim, fame, success, patriotic, co...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5521</td>\n",
       "      <td>vaccine, new, ban, khamenei, announced, work, ...</td>\n",
       "      <td>[many, bright, day, best, winning]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.8258</td>\n",
       "      <td>vaccine, covid, first, today, pfizer, dose, do...</td>\n",
       "      <td>[covid, vaccine, getting]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.6855</td>\n",
       "      <td>vaccine, covid, first, today, pfizer, dose, do...</td>\n",
       "      <td>[state, start, getting, monday, say]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Document_No  Dominant_Topic  Topic_Perc_Contrib  \\\n",
       "0            0             0.0              0.9197   \n",
       "1            1             1.0              0.6308   \n",
       "2            2             2.0              0.8509   \n",
       "3            3             0.0              0.9314   \n",
       "4            4             2.0              0.5936   \n",
       "5            5             0.0              0.9242   \n",
       "6            6             0.0              0.5541   \n",
       "7            7             1.0              0.5521   \n",
       "8            8             2.0              0.8258   \n",
       "9            9             2.0              0.6855   \n",
       "\n",
       "                                            Keywords  \\\n",
       "0  vaccine, dose, second, arm, side, effect, firs...   \n",
       "1  vaccine, new, ban, khamenei, announced, work, ...   \n",
       "2  vaccine, covid, first, today, pfizer, dose, do...   \n",
       "3  vaccine, dose, second, arm, side, effect, firs...   \n",
       "4  vaccine, covid, first, today, pfizer, dose, do...   \n",
       "5  vaccine, dose, second, arm, side, effect, firs...   \n",
       "6  vaccine, dose, second, arm, side, effect, firs...   \n",
       "7  vaccine, new, ban, khamenei, announced, work, ...   \n",
       "8  vaccine, covid, first, today, pfizer, dose, do...   \n",
       "9  vaccine, covid, first, today, pfizer, dose, do...   \n",
       "\n",
       "                                                Text  \n",
       "0  [folk, said, daikon, paste, could, treat, cyto...  \n",
       "1  [world, wrong, side, history, year, hopefully,...  \n",
       "2            [russian, vaccine, created, last, year]  \n",
       "3  [fact, immutable, senator, even, ethically, st...  \n",
       "4                           [explain, need, vaccine]  \n",
       "5  [anyone, useful, advice, guidance, whether, co...  \n",
       "6  [bit, sad, claim, fame, success, patriotic, co...  \n",
       "7                 [many, bright, day, best, winning]  \n",
       "8                          [covid, vaccine, getting]  \n",
       "9               [state, start, getting, monday, say]  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def format_topics_sentences(ldamodel=None, corpus=doc_term_matrix, texts=clean_txt):\n",
    "    # Init output\n",
    "    sent_topics_df = pd.DataFrame()\n",
    "\n",
    "    # Get main topic in each document\n",
    "    for i, row_list in enumerate(ldamodel[corpus]):\n",
    "        row = row_list[0] if ldamodel.per_word_topics else row_list            \n",
    "        # print(row)\n",
    "        row = sorted(row, key=lambda x: (x[1]), reverse=True)\n",
    "        # Get the Dominant topic, Perc Contribution and Keywords for each document\n",
    "        for j, (topic_num, prop_topic) in enumerate(row):\n",
    "            if j == 0:  # => dominant topic\n",
    "                wp = ldamodel.show_topic(topic_num)\n",
    "                topic_keywords = \", \".join([word for word, prop in wp])\n",
    "                sent_topics_df = sent_topics_df.append(pd.Series([int(topic_num), round(prop_topic,4), topic_keywords]), ignore_index=True)\n",
    "            else:\n",
    "                break\n",
    "    sent_topics_df.columns = ['Dominant_Topic', 'Perc_Contribution', 'Topic_Keywords']\n",
    "\n",
    "    # Add original text to the end of the output\n",
    "    contents = pd.Series(texts)\n",
    "    sent_topics_df = pd.concat([sent_topics_df, contents], axis=1)\n",
    "    return(sent_topics_df)\n",
    "\n",
    "\n",
    "df_topic_sents_keywords = format_topics_sentences(ldamodel=ldamodel, corpus=doc_term_matrix, texts=clean_txt)\n",
    "\n",
    "# Format\n",
    "df_dominant_topic = df_topic_sents_keywords.reset_index()\n",
    "df_dominant_topic.columns = ['Document_No', 'Dominant_Topic', 'Topic_Perc_Contrib', 'Keywords', 'Text']\n",
    "df_dominant_topic.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYwAAAEICAYAAABMGMOEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAdkElEQVR4nO3de5gdVZ3u8e9rwkXlEiANYhJMRgIjqDxgRM4wOgiKgA7BOTLCjBIxTkRRUbwAwoiD4uBlYHREPFwiFx0QkaPxDIoIeHBmBBKQiwHRGC5pE0hDIIDIJfDOH7UaNp3dSe1O77270+/neerpqrVWVf323sn+7Vq1qkq2iYiIWJsXdDuAiIgYHZIwIiKiliSMiIioJQkjIiJqScKIiIhakjAiIqKWJIzoKEnflPSP3Y6jnSSdK+lT3Y4jYrgp12GMbZLuArYBVgFPA7cB5wNn2n6mi6GtE0mfBba3/a5B6h9tWHwR8ATV6wd4v+3vtDfC0UvSxsCfgMcAA48DvwLOsH1pzW3sB3zd9vZtC7SD+xkrcoQRAH9te1PgZcApwDHAOd0Nqb1sb9I/AfdQvQf9ZaM2WUga38Hd7Vjev1cAFwJnSzqmg/uPTrOdaQxPwF3AmwaU7Q48A7yyLG9OddTRB9wNnAC8oNS9B/gv4DTgIWAx8BelfAmwHJjVsO1zgc+X+b2AXuDjpd0y4PCGtm+l+uX6cNnWZxvqplL9up1F9YV/P3B8qdsPeBJ4CngUuHkI78ELgdNLTL3Al4ENGra/CPgnYEV5zQc3rHsRcELD8sHALeV1/A7YZ5A4PgPcCTwC/Bp464D6DwK/KfW3Aq8q5fcCnwAWAo+VslcBvyifyS3A/g3bmdmwnSXAR0r5S4CflHUeAK4aJM6Ny3s/eUD5u6iOOjYry+9v2M8i4L2lfCuqI5RnyufzaCnbE7gOWAksLf+mxpd1xgFfp/o3uBK4mSph9X9W/1pey73AvwEbDbafbv+fG81T1wPI1OV/AE2+LEv5PcAHyvz5wA+BTam+qH8LzC5176Hqzjq8/Kf+fFn39PKfdt/yhbFJaX8uz08Yq4CTgA2AA8oXzhYN9a+iOhJ+NXAfcFCpm1q+tM4qXxi7UHUrvaLUfxb49lDfA+BLVF+4E6m67Obz/IS0CvhnYEPgTSXuaaX+2YQBvB54EHhjeR3bATsMEsc7gW1Lu3eX921iqXs3VbLeFRCwI+ULu3xJzgdeWt6LjUvbj5f39S3ly7I/vgeA3cv8VsCuZf404KvA+PK63jBInIMljBeX8jeW5QOBaSXeN1F9ee/c8B4uGrD+7sBrqf4dvZwqyRxR6mYCvwQ2K+/PzsDWpe6bwCXABKofN5cDJw62n0xDn9IlFYNZCmwpaRzVF9lxth+xfRfwL1RfYP3utP0t208D3wWmACfZfsL2T6l+7Q/Wh/xUafuU7cuovth2BLD9c9u32n7G9i1U3R5/NWD9f7L9J9s3U/3q3GUYXjvA31N96dxv+z6qRNj4mleVfT9p+2fAz4B3NNnO+4Bv2r66vI57bP+22Q5tf9f2stLuAuAPwGsatvMF279y5Q7bvQ2rn2Z7qe0/USUpgFPL+3o5cAXV59gf+86SNrX9gO1flfKnqJLOduV1XVP73ari/yPVr/8ty/I823eWeH8G/H/gL9ew/vW259t+2vbvgbN57vN+iipZ/HnV1AttLy9dcO8FjrL9kO2VVN2qh7QSe9SThBGDmUTV3TKR6tfm3Q11d5f6fvc1zP8JoHzJNpZtMsh+HrC9qmH5sf62kl4n6WpJfZJWAkeUeBrd22zddSFJVN0za3rNfbYfH1D/0iabmwL8vuZ+Z0u6RdJDkh6iSrL9r3dt21nSMP9S4B7bjSNaGuM/CPjfwD2SrpI0o5SfTPVD4WpJiyQdXSfuhvhfTPULf0VZPlDS9ZJWlNezN6t/fo3r7yTpx5Luk/QwVRddf/sfU51X+z/AfZK+IWmT8lo3ABY2vG8/ALZuJfaoJwkjViPptVRfLv9JdW7gKaoT4v22o/r1227/DswDptjenKrrQTXXHfLwv/JFey9rfs0Ty2ihxvqlTTa3hKp7ZY0k7UDV9z4H2NL2BKoumf7Xu7btNL7epSWeRs/Gb/uXtt9G1dX2U6ojN2yvtH2U7ZdRJZQTJO25ttgbvJ3qx8ENJXl8D/gcVdfRBOCqhtfT7PM5C7gReLntzai6KlVis+1Tbe9K1T25C3AU1TmmVWWdCWXa3PZWa9hPDFESRjxL0maS3kbVB//t0h30NHAxcLKkTSW9DDga+HYHQtoUWGH7cUm7A3/Xwrr3AVMlDfXf+IXAiZK2krQ1cDzPf80bAP8oaUNJewNvBr7fZDtnA++X9AZJL5A0pSSHgTahOjnbB7xA0hE8vxvvbOBYSbuosoOkyYPE/ouyjY9KGi/pzVTnkr4n6cWSDpG0GdUPgUcow4nLEcG0coS1spQ/Pcg+nlXeo1lUJ54/b/thqnMpG1ANZnhG0oFU56T63QdsXY4S+m0KrLT9qKSdgX9o2McekmaULqg/UnVzPm37KWAu8FVJE8t7M6W85sH2E0OUhBEAP5LUP2LmeOBUqpPY/T5M9Z90MdVRx79T/Sdttw8CJ5XYPkOVuOr6Xvn7gKQbh7Dvz1Bdk7IQuIlqJNiXGurvovpley/Ve3G47cUDN2L7F1Rdad+g+hK+Eljti972jVRHUAuofjVPK/P99RdQfS6XUI226j/Ju5rSVfY2qnMqD5T13lnOC0DV5393iecwqpFmUA2P/TlVErkG+Irta5vto7ijXM/y27KdD9r+QonhfqqRWz8qMRwEXNaw7s1UR493l66kLYGPAe8r2zyd6nxYvwlUAyb6R+LdDXyt1H2U6qhqQXlNP+G5ZNtsPzFEuXAvokW5GCzGqhxhRERELUkYERFRS7qkIiKilhxhRERELZ28UVlHTZw40VOnTu12GBERo8oNN9xwv+2eZnXrbcKYOnUqCxYsWHvDiIh4lqS7B6tLl1RERNSShBEREbUkYURERC1JGBERUUsSRkRE1JKEERERtbQ1YUiaK2m5pF8PKP+wpDskLZT0pYby48qDW+6Q9JaG8v1K2SJJx7Yz5oiIaK7d12GcS/Xg9vP7CyS9ker5vK+2/UR51gCSdqJ6rOLOVE/R+lnDcwNOp3reQC8wX9I827e1OfaIiGjQ1oRh+xpJUwcUfwA4xfYTpc3yUj4TuKiU3ylpEdVD4aF6iPtiAEkXlbZJGBERHdSNK713AF4v6WTgceATtudTPRK08WEtvTz3DOIlA8pf12zDkuZQPeKS7bYb+ITKiBiJFn3lz7sdwnpv+0/8Zli2042T3uOBLYA9gE8CF5dHQjZ7VrPXUL56oX2m7Rm2Z/T0NL0VSkREDFE3jjB6gUtd3Vf9eknPABNL+ZSGdpOpHrvIGsojIqJDunGE8QNgb4ByUntD4H6q5+4eImkjSdOA6cD1wHxgenk4/YZUJ8bndSHuiIgxra1HGJIuBPYCJkrqBU4E5gJzy1DbJ4FZ5WhjoaSLqU5mrwKOtP102c6HgMuBccBc2wvbGXdERKyu3aOkDh2k6l2DtD8ZOLlJ+WXAZcMYWkREtChXekdERC1JGBERUUsSRkRE1JKEERERtSRhRERELUkYERFRSxJGRETUkoQRERG1JGFEREQtSRgREVFLEkZERNSShBEREbUkYURERC1JGBERUUsSRkRE1JKEERERtSRhRERELW1NGJLmSlpeHsc6sO4TkixpYlmWpK9JWiTpFkm7NbSdJel3ZZrVzpgjIqK5dh9hnAvsN7BQ0hTgzcA9DcX7A9PLNAc4o7TdkupZ4K8DdgdOlLRFW6OOiIjVtDVh2L4GWNGk6jTgU4AbymYC57tyLTBB0rbAW4ArbK+w/SBwBU2SUEREtFfHz2FIOhD4g+2bB1RNApY0LPeWssHKm217jqQFkhb09fUNY9QREdHRhCHpRcDxwGeaVTcp8xrKVy+0z7Q9w/aMnp6eoQcaERGr6fQRxsuBacDNku4CJgM3SnoJ1ZHDlIa2k4GlayiPiIgO6mjCsH2r7a1tT7U9lSoZ7Gb7XmAecFgZLbUHsNL2MuByYF9JW5ST3fuWsoiI6KB2D6u9EPglsKOkXkmz19D8MmAxsAg4C/gggO0VwOeA+WU6qZRFREQHjW/nxm0fupb6qQ3zBo4cpN1cYO6wBhcRES3Jld4REVFLEkZERNSShBEREbUkYURERC1JGBERUUsSRkRE1JKEERERtSRhRERELUkYERFRSxJGRETUkoQRERG1JGFEREQtSRgREVFLEkZERNSShBEREbUkYURERC1JGBERUUu7H9E6V9JySb9uKPuypN9IukXS/5U0oaHuOEmLJN0h6S0N5fuVskWSjm1nzBER0Vy7jzDOBfYbUHYF8ErbrwZ+CxwHIGkn4BBg57LONySNkzQOOB3YH9gJOLS0jYiIDmprwrB9DbBiQNlPba8qi9cCk8v8TOAi20/YvhNYBOxepkW2F9t+EriotI2IiA7q9jmM9wI/LvOTgCUNdb2lbLDy1UiaI2mBpAV9fX1tCDciYuzqWsKQdDywCvhOf1GTZl5D+eqF9pm2Z9ie0dPTMzyBRkQEAOO7sVNJs4C3AfvY7v/y7wWmNDSbDCwt84OVR0REh3T8CEPSfsAxwIG2H2uomgccImkjSdOA6cD1wHxguqRpkjakOjE+r9NxR0SMdW09wpB0IbAXMFFSL3Ai1aiojYArJAFca/sI2wslXQzcRtVVdaTtp8t2PgRcDowD5tpe2M64IyJidW1NGLYPbVJ8zhranwyc3KT8MuCyYQwtIiJa1O1RUhERMUokYURERC1JGBERUUvthCHpJwPu+7SFpP9oT1gRETHStHKEsY3th/oXbD8IvHT4Q4qIiJGolYTxjKT++z4habs2xBMRESNUK8NqPwP8l6SryvIbgQ8Mf0gRETES1U4Ytv9D0u7A/6K6v9Mxtpe3LbKIiBhR1tolJWl6+ftqYBtgMfB74CWlLCIixoA6RxjHArOpHmI0kIE3DGtEERExIq01YdieXf6+vv3hRETESFX7HIakjYD3A39JdWTxC+As20+0KbaIiBhBWhkldR7wBHBWWT60lB0y3EFFRMTI00rC2Ml240nuKyTdPNwBRUTEyNTKhXs3SXpt/4Kk1wC/HP6QIiJiJGrlCGM34DpJi8vyNGChpF8Btr3bsEcXEREjRisJY2arG5c0l+rZ3cttv7KUbQl8F5gK3AX8re0HVT1+76vAAcBjwHts31jWmQWcUDb7edvntRpLRESsm9pdUrZ/D7wQeHOZNrb9+/5pkNXOBfYbUHYscKXt6cCVZRlgf6rneE8H5gBnwLMJ5kTgdcDuwImStqgbd0REDI9Wbm/+IeBiYLsyXSzpg2tax/Y1wIoBxTOpRldR/h7UUH6+K9cCEyRtC7wFuML2inKH3CtYPQlFRESbtdIlNQfY3fajAJK+APw38I0W97mN7WUAtpdJ2rqUTwKWNLTrLWWDlUdERAe1MkpKwFMNy0+VsuHSbFteQ/nqG5DmSFogaUFfX98whhYREa0kjAuAayWdIOkEqqOLoZx8vq90NVH+9t/xtheY0tBuMrB0DeWrsX2m7Rm2Z/T09AwhtIiIGEydu9WOB7D9JapuqceAPwFH2P7KEPY5D5hV5mcBP2woP0yVPYCVpevqcmDf8kjYLYB9S1lERHRQnXMY11Ndg4Ht+cD8uhuXdCGwFzBRUi/VaKdTqE6YzwbuAQ4uzS+jGlK7iCopHV72uULS5xr2e5LtgSfSIyKizeokjCGfp7B96CBV+zRpa+DIQbYzF5g71DgiImLd1UkYPZKOHqzS9qnDGE9ERIxQdRLGOGAThndEVEREjDJ1EsYy2ye1PZKIiBjR6gyrrXVkkdt1RESs3+okjNVOUA/iynUJJCIiRra1JowWhrDmHEdExHqslSu916bp7ToiImL9MJwJIyIi1mPDmTDSJRURsR5r5XkYF6ylrO7J8YiIGIVaOcLYuXFB0jjgNf3Lub9TRMT6rc7dao+T9AjwakkPl+kRqtuS/3Atq0dExHqizrDaf7a9KfBl25uVaVPbW9k+rgMxRkTECFD7Ea22j5M0CXhZ43rlud0REbGeq50wJJ0CHALcBjxdig0kYUREjAG1EwbwdmBH20+0K5iIiBi5WkkYi4ENgPUyYfzNlNndDmG9d+mSc7odQkSsg1YSxmPATZKupCFp2P7IUHYs6WPA+6i6tW6leiTrtsBFwJbAjcC7bT8paSPgfKphvA8A77R911D2GxERQ9PKdRjzgM8B/w3c0DC1rJw8/wgww/YrqR7SdAjwReA029OBB4H+n/2zgQdtbw+cVtpFREQHtTJK6rw27PuFkp4CXgQsA/YG/q7Unwd8FjgDmFnmAS4Bvi5J5TngERHRAa3cGmS6pEsk3SZpcf80lJ3a/gPwFeAeqkSxkupo5SHbq0qzXmBSmZ8ELCnrrirtt2oS4xxJCyQt6OvrG0poERExiFa6pL5F9Wt/FfBGqnMKq91fqo7ydL6ZwDTgpcCLgf2bNO0/gmh2Y8PVji5sn2l7hu0ZPT09QwktIiIG0UrCeKHtKwHZvtv2Z6m6kIbiTcCdtvtsPwVcCvwFMEFSfzfZZGBpme8FpgCU+s2B3LsqIqKDWkkYj0t6AfA7SR+S9HZg6yHu9x5gD0kvkiSqO93eBlwNvKO0mcVz96qaV5Yp9Vfl/EVERGe1kjA+SnVy+iNUw1vfzXNf4i2xfR3VyesbqYbUvgA4EzgGOFrSIqpzFP0D988BtirlRwPHDmW/ERExdK2MkppfZh+lumZindg+EThxQPFiYPcmbR8HDl7Xfcb66ZL9D+t2COu9d/z4/G6HECNAK/eS2gH4JKvffHCo5zEiImIUaeVK7+8B3wTO4rmbD0ZExBjRSsJYZfuMtkUSEREjWisnvX8k6YOStpW0Zf/UtsgiImJEaeUIo39E1Ccbygz82fCFExERI1Uro6SmtTOQiIgY2daaMCTtbfsqSX/TrN72pcMfVkREjDR1jjD+CrgK+Osmdaa6rUdERKzn1powygV22F7ni/UiImL0auXCvQnAYcBUnn/h3pCeuBcREaNLK6OkLgOupbr30zPtCSciIkaqVhLGxraPblskERExorVy4d4Fkv4hF+5FRIxNrRxhPAl8GTie5552lwv3IiLGiFYSxtHA9rbvb1cwERExcrXSJbUQeKxdgURExMjWyhHG08BNkq4GnugvzLDaiIixoZWE8YMyDYtyXcfZwCupzoW8F7gD+C7VtR53AX9r+8Hy3O+vAgdQHeW8x/aNwxVLRESsXSs3HzxP0obADqXoDttPrcO+vwr8xPY7ynZfBHwauNL2KZKOpXp29zHA/sD0Mr0OOKP8jYiIDql9DkPSXsDvgNOBbwC/lfSGoexU0mbAG4BzAGw/afshYCZwXml2HnBQmZ8JnO/KtcAESdsOZd8RETE0rXRJ/Quwr+074NlnfF8IvGYI+/0zoA/4lqRdgBuAo4BtbC8DsL1M0tal/SRgScP6vaVsWeNGJc0B5gBst912QwgrIiIG08ooqQ36kwWA7d8CGwxxv+OB3YAzbO8K/JGq+2kwalLm1QrsM23PsD2jp6dniKFFREQzrSSMBZLOkbRXmc6iOjIYil6g1/Z1ZfkSqgRyX39XU/m7vKH9lIb1JwNLh7jviIgYglYSxgeorsX4CFX30W3AEUPZqe17gSWSdixF+5TtzeO5R8HOAn5Y5ucBh6myB7Cyv+sqIiI6o5VRUk9IugC4wHbfMOz7w8B3ygipxcDhVAnsYkmzgXuAg0vby6iG1C6iGlabZ3NERHRYnUe0CjgR+BDVuQRJehr4N9snDXXHtm8CZjSp2qdJWwNHDnVfERGx7up0SX0U2BN4re2tbG9JdQ3EnpI+1tboIiJixKiTMA4DDrV9Z3+B7cXAu0pdRESMAXUSxgbN7lBbzmMMdVhtRESMMnUSxpNDrIuIiPVInVFSu0h6uEm5gI2HOZ6IiBih1powbI/rRCARETGytXLhXkREjGFJGBERUUsSRkRE1JKEERERtSRhRERELUkYERFRSxJGRETUkoQRERG1JGFEREQtSRgREVFLEkZERNTS1YQhaZykX0n6f2V5mqTrJP1O0nfL41uRtFFZXlTqp3Yz7oiIsajbRxhHAbc3LH8ROM32dOBBYHYpnw08aHt74LTSLiIiOqhrCUPSZOCtwNllWcDewCWlyXnAQWV+Zlmm1O9T2kdERId08wjjX4FPAc+U5a2Ah2yvKsu9wKQyPwlYAlDqV5b2zyNpjqQFkhb09fW1M/aIiDGnKwlD0tuA5bZvaCxu0tQ16p4rsM+0PcP2jJ6enmGINCIi+tV54l477AkcKOkAqqf2bUZ1xDFB0vhyFDEZWFra9wJTgF5J44HNgRWdDzsiYuzqyhGG7eNsT7Y9FTgEuMr23wNXA+8ozWYBPyzz88oypf4q26sdYURERPt0e5TUQMcAR0taRHWO4pxSfg6wVSk/Gji2S/FFRIxZ3eqSepbtnwM/L/OLgd2btHkcOLijgUVExPOMtCOMiIgYoZIwIiKiliSMiIioJQkjIiJqScKIiIhakjAiIqKWJIyIiKglCSMiImpJwoiIiFqSMCIiopYkjIiIqCUJIyIiaknCiIiIWpIwIiKiliSMiIioJQkjIiJqScKIiIhaupIwJE2RdLWk2yUtlHRUKd9S0hWSflf+blHKJelrkhZJukXSbt2IOyJiLOvWEcYq4OO2XwHsARwpaSeqZ3VfaXs6cCXPPbt7f2B6meYAZ3Q+5IiIsa0rCcP2Mts3lvlHgNuBScBM4LzS7DzgoDI/EzjflWuBCZK27XDYERFjWtfPYUiaCuwKXAdsY3sZVEkF2Lo0mwQsaVitt5QN3NYcSQskLejr62tn2BERY05XE4akTYDvAx+1/fCamjYp82oF9pm2Z9ie0dPTM1xhRkQEXUwYkjagShbfsX1pKb6vv6up/F1eynuBKQ2rTwaWdirWiIjo3igpAecAt9s+taFqHjCrzM8CfthQflgZLbUHsLK/6yoiIjpjfJf2uyfwbuBWSTeVsk8DpwAXS5oN3AMcXOouAw4AFgGPAYd3NtyIiOhKwrD9nzQ/LwGwT5P2Bo5sa1AREbFGXR8lFRERo0MSRkRE1JKEERERtSRhRERELUkYERFRSxJGRETUkoQRERG1JGFEREQtSRgREVFLEkZERNSShBEREbUkYURERC1JGBERUUsSRkRE1JKEERERtSRhRERELUkYERFRy6hKGJL2k3SHpEWSju12PBERY8moSRiSxgGnA/sDOwGHStqpu1FFRIwdoyZhALsDi2wvtv0kcBEws8sxRUSMGeO7HUALJgFLGpZ7gdc1NpA0B5hTFh+VdEeHYuuGicD93Q6iFdLcbocwkoyuz08XdDuCkWR0fXYAn1QrrV82WMVoShjNXrGft2CfCZzZmXC6S9IC2zO6HUcMTT6/0Wssf3ajqUuqF5jSsDwZWNqlWCIixpzRlDDmA9MlTZO0IXAIMK/LMUVEjBmjpkvK9ipJHwIuB8YBc20v7HJY3TQmut7WY/n8Rq8x+9nJ9tpbRUTEmDeauqQiIqKLkjAiIqKWJIwRbm23Q5G0kaTvlvrrJE3tfJTRjKS5kpZL+vUg9ZL0tfLZ3SJpt07HGM1JmiLpakm3S1oo6agmbcbc55eEMYLVvB3KbOBB29sDpwFf7GyUsQbnAvutoX5/YHqZ5gBndCCmqGcV8HHbrwD2AI5s8n9vzH1+SRgjW53bocwEzivzlwD7SGrpss5oD9vXACvW0GQmcL4r1wITJG3bmehiTWwvs31jmX8EuJ3qbhONxtznl4QxsjW7HcrAf7TPtrG9ClgJbNWR6GJd1fl8o8tKN++uwHUDqsbc55eEMbKt9XYoNdvEyJTPboSTtAnwfeCjth8eWN1klfX680vCGNnq3A7l2TaSxgObs+ZukBg5crubEUzSBlTJ4ju2L23SZMx9fkkYI1ud26HMA2aV+XcAVzlXY44W84DDymibPYCVtpd1O6ioRkAB5wC32z51kGZj7vMbNbcGGYsGux2KpJOABbbnUf2jvkDSIqoji0O6F3E0knQhsBcwUVIvcCKwAYDtbwKXAQcAi4DHgMO7E2k0sSfwbuBWSTeVsk8D28HY/fxya5CIiKglXVIREVFLEkZERNSShBEREbUkYURERC1JGBERUUsSRkRE1JKEERERtfwPX85efx+/y80AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "a = pd.DataFrame(df_dominant_topic['Dominant_Topic'].value_counts())\n",
    "sns.barplot(a.index,a.Dominant_Topic, palette='inferno')\n",
    "plt.title(\"Dominant Topic across Dataset\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Frequency Distribution of Word Counts in Documents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "doc_lens = [len(d) for d in df_dominant_topic.Text]\n",
    "\n",
    "# Plot\n",
    "plt.figure(figsize=(14,7))\n",
    "plt.hist(doc_lens, bins = 50, color='navy')\n",
    "plt.text(750, 100, \"Mean   : \" + str(round(np.mean(doc_lens))))\n",
    "plt.text(750,  90, \"Median : \" + str(round(np.median(doc_lens))))\n",
    "plt.text(750,  80, \"Stdev   : \" + str(round(np.std(doc_lens))))\n",
    "plt.text(750,  70, \"1%ile    : \" + str(round(np.quantile(doc_lens, q=0.01))))\n",
    "plt.text(750,  60, \"99%ile  : \" + str(round(np.quantile(doc_lens, q=0.99))))\n",
    "\n",
    "plt.gca().set(xlim=(0, 50), ylabel='Number of Documents', xlabel='Document Word Count')\n",
    "plt.tick_params(size=16)\n",
    "plt.xticks(np.linspace(0,50,4))\n",
    "plt.title('Distribution of Document Word Counts', fontdict=dict(size=22))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Top words in each Topic Cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cols = [color for name, color in mcolors.TABLEAU_COLORS.items()]  # more colors: 'mcolors.XKCD_COLORS'\n",
    "\n",
    "cloud = WordCloud(\n",
    "                  background_color='white',\n",
    "                  width=2500,\n",
    "                  height=1800,\n",
    "                  max_words=20,\n",
    "                  colormap='Spectral',\n",
    "                  color_func=lambda *args, **kwargs: cols[i],\n",
    "                  prefer_horizontal=1.0)\n",
    "\n",
    "topics = ldamodel.show_topics(formatted=False)\n",
    "\n",
    "fig, axes = plt.subplots(1, 3, figsize=(15,15), sharex=True, sharey=True)\n",
    "\n",
    "for i, ax in enumerate(axes.flatten()):\n",
    "    fig.add_subplot(ax)\n",
    "    topic_words = dict(topics[i][1])\n",
    "    cloud.generate_from_frequencies(topic_words, max_font_size=300)\n",
    "    plt.gca().imshow(cloud)\n",
    "    plt.gca().set_title('Topic ' + str(i), fontdict=dict(size=16))\n",
    "    plt.gca().axis('off')\n",
    "\n",
    "\n",
    "plt.subplots_adjust(wspace=0, hspace=0)\n",
    "plt.axis('off')\n",
    "plt.margins(x=0, y=0)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Topics wise Sentiment Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Conducting Sentiment Analysis to draw further Inference\n",
    "analyser= SentimentIntensityAnalyzer() \n",
    "\n",
    "def get_vader_sentiment(sent):\n",
    "    return analyser.polarity_scores(sent)['compound']\n",
    "\n",
    "tweets['sent_vader']= tweets['Clean_Tweets'].apply(get_vader_sentiment)\n",
    "\n",
    "sent_pred=[]\n",
    "for i in range(len(tweets['sent_vader'])):\n",
    "    if tweets['sent_vader'][i]>=0.05:\n",
    "        sent_pred.append('positive')\n",
    "    elif tweets['sent_vader'][i]<=-0.05:\n",
    "        sent_pred.append('negative')\n",
    "    else:\n",
    "        sent_pred.append('neutral')  \n",
    "\n",
    "tweets['sent_pred']= sent_pred\n",
    "merge1 = pd.concat([df_dominant_topic,tweets[['sent_pred','sent_vader']]],axis=1)\n",
    "m1 = pd.DataFrame(merge1.groupby(['Dominant_Topic','sent_pred'])['Text'].count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1f55fe17eb8>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cross_tab= pd.crosstab(index=merge1['Dominant_Topic'],columns=merge1['sent_pred'])\n",
    "cross_tab.plot(kind='bar', figsize=(10,7), colormap=\"viridis\")\n",
    "plt.xticks(rotation=0)\n",
    "plt.title(\"Sentiments in Dominant Topics\")\n",
    "ax= plt.subplot(111)\n",
    "ax.legend(loc=\"upper center\",bbox_to_anchor=(00.5,1.00),ncol=4,fancybox=True,shadow=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Analysing the Topic Cluster, \n",
    "  - Cluster-1 has a positive outlook with terms such as 'grateful', 'good', 'thanks' & 'received' \n",
    "  - Cluster-2 has a concerned outlook with terms such as 'emergency', 'injection', 'sore' & 'health'\n",
    "  - Cluster-3 has a negative outlook with terms such as 'ban', 'red', 'death', 'mutation', 'protect' & 'please'\n",
    "  \n",
    "*  Most Tweets belong to Cluster-1, Cluster-2 & Cluster-3 (descending order) \n",
    "\n",
    "* It is seen that percentage of positive sentiment tweet is highest in Cluster-1 justifying the positive outlook in the Tweets topics\n",
    "\n",
    "* In Cluster-2, Neutral statement Tweets are highest\n",
    "\n",
    "* In Cluster-3, it is seen thet in comparison to other two Topic Clusters, the number or count of Negative sentiment Tweets in highest Cluster -3 with 207 tweets"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}