{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b9900828",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\PC\\anaconda3\\lib\\site-packages\\gensim\\similarities\\__init__.py:15: UserWarning: The gensim.similarities.levenshtein submodule is disabled, because the optional Levenshtein package <https://pypi.org/project/python-Levenshtein/> is unavailable. Install Levenhstein (e.g. `pip install python-Levenshtein`) to suppress this warning.\n",
      "  warnings.warn(msg)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import spacy\n",
    "import gensim.corpora as co\n",
    "from gensim.utils import simple_preprocess\n",
    "from gensim.models import CoherenceModel\n",
    "import pyLDAvis\n",
    "import pyLDAvis.gensim_models\n",
    "from nltk.corpus import stopwords\n",
    "import gensim\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\", category=DeprecationWarning) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "386fb83e",
   "metadata": {},
   "outputs": [],
   "source": [
    "movies= pd.read_csv('Final1.0.tsv', sep='\\t').dropna(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "38cd2972",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>titleId</th>\n",
       "      <th>averageRating</th>\n",
       "      <th>numVotes</th>\n",
       "      <th>title</th>\n",
       "      <th>startYear</th>\n",
       "      <th>runtimeMinutes</th>\n",
       "      <th>genres</th>\n",
       "      <th>synopsis</th>\n",
       "      <th>synopsis_allowed_pos</th>\n",
       "      <th>keywords</th>\n",
       "      <th>scores</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tt0111161</td>\n",
       "      <td>9.3</td>\n",
       "      <td>2405502</td>\n",
       "      <td>The Shawshank Redemption</td>\n",
       "      <td>1994</td>\n",
       "      <td>142</td>\n",
       "      <td>Drama</td>\n",
       "      <td>In 1947, Andy Dufresne (Tim Robbins), a banker...</td>\n",
       "      <td>banker wife lover golf state death penalty con...</td>\n",
       "      <td>prison,warden,inmate,solitary,parole,cell,rock...</td>\n",
       "      <td>0.43173508179310854,0.3378515191216742,0.24607...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tt0468569</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2365212</td>\n",
       "      <td>The Dark Knight</td>\n",
       "      <td>2008</td>\n",
       "      <td>152</td>\n",
       "      <td>Action,Crime,Drama</td>\n",
       "      <td>The movie begins with a gang of men with clown...</td>\n",
       "      <td>movie gang man clown mask bank mob large porti...</td>\n",
       "      <td>mob,coin,clown,ferry,member,building,cell,deto...</td>\n",
       "      <td>0.31071887549768684,0.30611526621129936,0.2273...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tt1375666</td>\n",
       "      <td>8.8</td>\n",
       "      <td>2122723</td>\n",
       "      <td>Inception</td>\n",
       "      <td>2010</td>\n",
       "      <td>148</td>\n",
       "      <td>Action,Adventure,Sci-Fi</td>\n",
       "      <td>A young man, exhausted and delirious, washes u...</td>\n",
       "      <td>young man exhausted delirious beach see young ...</td>\n",
       "      <td>dream,limbo,team,subconscious,projection,kick,...</td>\n",
       "      <td>0.5233624066972737,0.27957745198681444,0.22574...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tt0137523</td>\n",
       "      <td>8.8</td>\n",
       "      <td>1899050</td>\n",
       "      <td>Fight Club</td>\n",
       "      <td>1999</td>\n",
       "      <td>139</td>\n",
       "      <td>Drama</td>\n",
       "      <td>We back out of the webbing of neurons and brai...</td>\n",
       "      <td>webbing neuron brain cell title credit pore sw...</td>\n",
       "      <td>narrator,soap,member,fight,club,consumerism,in...</td>\n",
       "      <td>0.9295753501136745,0.08846173003330136,0.07087...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tt0110912</td>\n",
       "      <td>8.9</td>\n",
       "      <td>1868911</td>\n",
       "      <td>Pulp Fiction</td>\n",
       "      <td>1994</td>\n",
       "      <td>154</td>\n",
       "      <td>Crime,Drama</td>\n",
       "      <td>Late one morning in the Hawthorne Grill, a res...</td>\n",
       "      <td>morning restaurant young couple discuss pro co...</td>\n",
       "      <td>watch,tyranny,apartment,strip,pawn,miracle,car...</td>\n",
       "      <td>0.2089635652704242,0.20058534799411254,0.17449...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7133</th>\n",
       "      <td>tt4587656</td>\n",
       "      <td>6.3</td>\n",
       "      <td>7088</td>\n",
       "      <td>The Star</td>\n",
       "      <td>2017</td>\n",
       "      <td>86</td>\n",
       "      <td>Adventure,Animation,Comedy</td>\n",
       "      <td>The film opens in Nazareth where Mary (voice o...</td>\n",
       "      <td>film voice angel voice bear son pygmy mouse vo...</td>\n",
       "      <td>donkey,voice,manger,camel,messiah,dog,animal,b...</td>\n",
       "      <td>0.5604545151752331,0.40825124839629895,0.28761...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7134</th>\n",
       "      <td>tt0035238</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7087</td>\n",
       "      <td>Random Harvest</td>\n",
       "      <td>1942</td>\n",
       "      <td>126</td>\n",
       "      <td>Adventure,Drama,Family</td>\n",
       "      <td>A World War I veteran is languishing in an Eng...</td>\n",
       "      <td>english asylum shell shock amnesia remember na...</td>\n",
       "      <td>memory,asylum,lapse,amnesia,marriage,sensing,t...</td>\n",
       "      <td>0.46300302647741065,0.30390728167539177,0.2640...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7135</th>\n",
       "      <td>tt0075765</td>\n",
       "      <td>6.8</td>\n",
       "      <td>7087</td>\n",
       "      <td>Black Sunday</td>\n",
       "      <td>1977</td>\n",
       "      <td>143</td>\n",
       "      <td>Adventure,Crime,Drama</td>\n",
       "      <td>On November 12, 1975, Dahlia Iyad (Marthe Kell...</td>\n",
       "      <td>airport taxi town narrow alley bazaar back sea...</td>\n",
       "      <td>blimp,helicopter,gondola,stadium,israeli,compo...</td>\n",
       "      <td>0.727998379372826,0.19061784921474606,0.175583...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7136</th>\n",
       "      <td>tt0087197</td>\n",
       "      <td>6.5</td>\n",
       "      <td>7087</td>\n",
       "      <td>Electric Dreams</td>\n",
       "      <td>1984</td>\n",
       "      <td>95</td>\n",
       "      <td>Comedy,Drama,Music</td>\n",
       "      <td>Lonely architect Miles (Von Dohlen) purchases ...</td>\n",
       "      <td>lonely architect fancy new cone personal compu...</td>\n",
       "      <td>computer,musical,talent,human,sexology,romance...</td>\n",
       "      <td>0.3092893491517544,0.3039393485587254,0.274328...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7137</th>\n",
       "      <td>tt0058725</td>\n",
       "      <td>6.4</td>\n",
       "      <td>7084</td>\n",
       "      <td>Viva Las Vegas</td>\n",
       "      <td>1964</td>\n",
       "      <td>85</td>\n",
       "      <td>Comedy,Musical</td>\n",
       "      <td>Lucky Jackson (Elvis Presley) goes to Las Vega...</td>\n",
       "      <td>participate city first annual race car need ne...</td>\n",
       "      <td>hotel,win,contest,affection,necessary,talent,e...</td>\n",
       "      <td>0.3520256599161476,0.2478492041831934,0.222146...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7138 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        titleId  averageRating  numVotes                     title  startYear  \\\n",
       "0     tt0111161            9.3   2405502  The Shawshank Redemption       1994   \n",
       "1     tt0468569            9.0   2365212           The Dark Knight       2008   \n",
       "2     tt1375666            8.8   2122723                 Inception       2010   \n",
       "3     tt0137523            8.8   1899050                Fight Club       1999   \n",
       "4     tt0110912            8.9   1868911              Pulp Fiction       1994   \n",
       "...         ...            ...       ...                       ...        ...   \n",
       "7133  tt4587656            6.3      7088                  The Star       2017   \n",
       "7134  tt0035238            8.0      7087            Random Harvest       1942   \n",
       "7135  tt0075765            6.8      7087              Black Sunday       1977   \n",
       "7136  tt0087197            6.5      7087           Electric Dreams       1984   \n",
       "7137  tt0058725            6.4      7084            Viva Las Vegas       1964   \n",
       "\n",
       "     runtimeMinutes                      genres  \\\n",
       "0               142                       Drama   \n",
       "1               152          Action,Crime,Drama   \n",
       "2               148     Action,Adventure,Sci-Fi   \n",
       "3               139                       Drama   \n",
       "4               154                 Crime,Drama   \n",
       "...             ...                         ...   \n",
       "7133             86  Adventure,Animation,Comedy   \n",
       "7134            126      Adventure,Drama,Family   \n",
       "7135            143       Adventure,Crime,Drama   \n",
       "7136             95          Comedy,Drama,Music   \n",
       "7137             85              Comedy,Musical   \n",
       "\n",
       "                                               synopsis  \\\n",
       "0     In 1947, Andy Dufresne (Tim Robbins), a banker...   \n",
       "1     The movie begins with a gang of men with clown...   \n",
       "2     A young man, exhausted and delirious, washes u...   \n",
       "3     We back out of the webbing of neurons and brai...   \n",
       "4     Late one morning in the Hawthorne Grill, a res...   \n",
       "...                                                 ...   \n",
       "7133  The film opens in Nazareth where Mary (voice o...   \n",
       "7134  A World War I veteran is languishing in an Eng...   \n",
       "7135  On November 12, 1975, Dahlia Iyad (Marthe Kell...   \n",
       "7136  Lonely architect Miles (Von Dohlen) purchases ...   \n",
       "7137  Lucky Jackson (Elvis Presley) goes to Las Vega...   \n",
       "\n",
       "                                   synopsis_allowed_pos  \\\n",
       "0     banker wife lover golf state death penalty con...   \n",
       "1     movie gang man clown mask bank mob large porti...   \n",
       "2     young man exhausted delirious beach see young ...   \n",
       "3     webbing neuron brain cell title credit pore sw...   \n",
       "4     morning restaurant young couple discuss pro co...   \n",
       "...                                                 ...   \n",
       "7133  film voice angel voice bear son pygmy mouse vo...   \n",
       "7134  english asylum shell shock amnesia remember na...   \n",
       "7135  airport taxi town narrow alley bazaar back sea...   \n",
       "7136  lonely architect fancy new cone personal compu...   \n",
       "7137  participate city first annual race car need ne...   \n",
       "\n",
       "                                               keywords  \\\n",
       "0     prison,warden,inmate,solitary,parole,cell,rock...   \n",
       "1     mob,coin,clown,ferry,member,building,cell,deto...   \n",
       "2     dream,limbo,team,subconscious,projection,kick,...   \n",
       "3     narrator,soap,member,fight,club,consumerism,in...   \n",
       "4     watch,tyranny,apartment,strip,pawn,miracle,car...   \n",
       "...                                                 ...   \n",
       "7133  donkey,voice,manger,camel,messiah,dog,animal,b...   \n",
       "7134  memory,asylum,lapse,amnesia,marriage,sensing,t...   \n",
       "7135  blimp,helicopter,gondola,stadium,israeli,compo...   \n",
       "7136  computer,musical,talent,human,sexology,romance...   \n",
       "7137  hotel,win,contest,affection,necessary,talent,e...   \n",
       "\n",
       "                                                 scores  \n",
       "0     0.43173508179310854,0.3378515191216742,0.24607...  \n",
       "1     0.31071887549768684,0.30611526621129936,0.2273...  \n",
       "2     0.5233624066972737,0.27957745198681444,0.22574...  \n",
       "3     0.9295753501136745,0.08846173003330136,0.07087...  \n",
       "4     0.2089635652704242,0.20058534799411254,0.17449...  \n",
       "...                                                 ...  \n",
       "7133  0.5604545151752331,0.40825124839629895,0.28761...  \n",
       "7134  0.46300302647741065,0.30390728167539177,0.2640...  \n",
       "7135  0.727998379372826,0.19061784921474606,0.175583...  \n",
       "7136  0.3092893491517544,0.3039393485587254,0.274328...  \n",
       "7137  0.3520256599161476,0.2478492041831934,0.222146...  \n",
       "\n",
       "[7138 rows x 11 columns]"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "9dfc4f80",
   "metadata": {},
   "outputs": [],
   "source": [
    "plots=movies.iloc[:,-2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "id": "a0706973",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       prison,warden,inmate,solitary,parole,cell,rock...\n",
       "1       mob,coin,clown,ferry,member,building,cell,deto...\n",
       "2       dream,limbo,team,subconscious,projection,kick,...\n",
       "3       narrator,soap,member,fight,club,consumerism,in...\n",
       "4       watch,tyranny,apartment,strip,pawn,miracle,car...\n",
       "                              ...                        \n",
       "7133    donkey,voice,manger,camel,messiah,dog,animal,b...\n",
       "7134    memory,asylum,lapse,amnesia,marriage,sensing,t...\n",
       "7135    blimp,helicopter,gondola,stadium,israeli,compo...\n",
       "7136    computer,musical,talent,human,sexology,romance...\n",
       "7137    hotel,win,contest,affection,necessary,talent,e...\n",
       "Name: keywords, Length: 7138, dtype: object"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "id": "22f15ce3",
   "metadata": {},
   "outputs": [],
   "source": [
    "texts= plots.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "ac964ab2",
   "metadata": {},
   "outputs": [],
   "source": [
    "texts=texts.reshape(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "id": "86341e9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#plots['texts']= plots['0'].astype(str)+' '+plots['1'].astype(str)+' '+plots['2'].astype(str)+' '+plots['3'].astype(str)+' '+plots['4'].astype(str)+' '+plots['5'].astype(str)\\\n",
    "             #       +' '+plots['6'].astype(str)+' '+plots['7'].astype(str)+' '+plots['8'].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "id": "e09de0b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7138,)"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "texts.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f490d8a6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d3c5cfda",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "578a16b2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "81e9ffcd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "563668e6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bd825ecb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "74a3288f",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'list' object has no attribute 'words'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-118-9a9f624e3ddf>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mstopwords\u001b[0m\u001b[1;33m=\u001b[0m \u001b[0mstopwords\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwords\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'english'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m stopwords.extend(['nude','nudity','male','female','men','women','boy','girl','age','frontal','pubic','woman','man','hair',\\\n\u001b[0;32m      3\u001b[0m                  'underwear', 'pant','sex',\"'s\", 'aaaxxxyyy','sexual', 'father'])\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'list' object has no attribute 'words'"
     ]
    }
   ],
   "source": [
    "stopwords= stopwords.words('english')\n",
    "stopwords.extend(['nude','nudity','male','female','men','women','boy','girl','age','frontal','pubic','woman','man','hair',\\\n",
    "                 'underwear', 'pant','sex',\"'s\", 'aaaxxxyyy','sexual', 'father'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "4d02b97c",
   "metadata": {},
   "outputs": [],
   "source": [
    "stopwords.extend(['go','none'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "02689294",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7f1d8ccf",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "818eea61",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "6478e144",
   "metadata": {},
   "outputs": [],
   "source": [
    "def lemma(texts, allowed_pos=['NOUN',\"ADJ\",'JJ','JJR', 'VB']):\n",
    "    nlp= spacy.load('en_core_web_sm', disable=['parser', 'ner'])\n",
    "    text_out=[]\n",
    "    i=1\n",
    "    for text in texts:\n",
    "        try:\n",
    "            print(i)\n",
    "            doc= nlp(text)\n",
    "            #print(doc)\n",
    "            new_text=[]\n",
    "            for token in doc:\n",
    "                if token.text not in stopwords:\n",
    "                    #if token.pos_ in allowed_pos:\n",
    "                        #print('\\nt***\\n',token.lemma_)\n",
    "                    new_text.append(token.lemma_)\n",
    "\n",
    "            final=' '.join(new_text)\n",
    "            #print('\\n***\\n',final)\n",
    "            text_out.append(final)\n",
    "            i+=1\n",
    "        except:\n",
    "            pass\n",
    "    \n",
    "    return text_out\n",
    "        \n",
    "#lemma_texts= lemma(texts)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "id": "f6c738fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "lemma_texts= texts[:1000]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6eaf1ab",
   "metadata": {},
   "source": [
    "import joblib\n",
    "\n",
    "lemma_texts=joblib.load('lemma_texts_plots.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f2ca57c2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "223ea0e2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ba169168",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "id": "ada922e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def gen_words(texts):\n",
    "    final=[]\n",
    "    i=1\n",
    "    for text in texts:\n",
    "        print(i)\n",
    "        new= simple_preprocess(text, deacc= True)\n",
    "        final.append(new)\n",
    "        i+=1\n",
    "    return final\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "id": "86be7967",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "data_words= gen_words(lemma_texts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "19ef81be",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "32edfa8f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "id": "8a03c4c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Build the bigram and trigram models\n",
    "bigram = gensim.models.Phrases(data_words, min_count=3, threshold=50) # higher threshold fewer phrases.\n",
    "trigram = gensim.models.Phrases(bigram[data_words], threshold=50)  \n",
    "\n",
    "# Faster way to get a sentence clubbed as a trigram/bigram\n",
    "bigram = gensim.models.phrases.Phraser(bigram)\n",
    "trigram = gensim.models.phrases.Phraser(trigram)\n",
    "\n",
    "def make_bi(texts):\n",
    "    return ([bigram[doc] for doc in texts])\n",
    "\n",
    "def make_tri(texts):\n",
    "    return ([trigram[doc] for doc in texts])\n",
    "\n",
    "data_bigrams= make_bi(data_words)\n",
    "data_bi_tri= make_tri(data_bigrams)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e80ff155",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "id": "bdc45c2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['scheme',\n",
       " 'letter',\n",
       " 'contractor',\n",
       " 'expertise',\n",
       " 'morning',\n",
       " 'promise',\n",
       " 'giant',\n",
       " 'order',\n",
       " 'tax',\n",
       " 'month']"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_bi_tri[0][-10:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f4bb3979",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e3b28b9b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1614ce76",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "id": "2cbeeda7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from gensim.models import TfidfModel\n",
    "\n",
    "id2word= co.Dictionary(data_bi_tri)\n",
    "\n",
    "\n",
    "\n",
    "corpus= [ id2word.doc2bow(text) for text in data_bi_tri]\n",
    "\n",
    "tfidf = TfidfModel(corpus, id2word)\n",
    "\n",
    "low_value= 0.02\n",
    "words=[]\n",
    "words_missin_in_tfidf=[]\n",
    "\n",
    "\n",
    "for i in range(0, len(corpus)):\n",
    "    bow = corpus[i]\n",
    "    #print(bow[:30])\n",
    "    low_value_words = [] #reinitialize to be safe. You can skip this.\n",
    "    low_value_words = [id for id, value in tfidf[bow] if value < low_value]\n",
    "    new_bow = [b for b in bow if b[0] not in low_value_words]\n",
    "\n",
    "    #reassign        \n",
    "    corpus[i] = new_bow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "id": "d75d3d74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0, 1),\n",
       " (1, 1),\n",
       " (2, 1),\n",
       " (3, 1),\n",
       " (4, 1),\n",
       " (5, 1),\n",
       " (6, 1),\n",
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       " (8, 1),\n",
       " (9, 1),\n",
       " (10, 1),\n",
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       " (13, 1),\n",
       " (14, 1),\n",
       " (15, 1),\n",
       " (16, 1),\n",
       " (17, 1),\n",
       " (18, 1),\n",
       " (19, 1),\n",
       " (20, 1),\n",
       " (21, 1),\n",
       " (22, 1),\n",
       " (23, 1),\n",
       " (24, 1),\n",
       " (25, 1),\n",
       " (26, 1),\n",
       " (27, 1),\n",
       " (28, 1),\n",
       " (29, 1),\n",
       " (30, 1),\n",
       " (31, 1),\n",
       " (32, 1),\n",
       " (33, 1),\n",
       " (34, 1),\n",
       " (35, 1),\n",
       " (36, 1),\n",
       " (37, 1),\n",
       " (38, 1),\n",
       " (39, 1),\n",
       " (40, 1),\n",
       " (41, 1),\n",
       " (42, 1),\n",
       " (43, 1),\n",
       " (44, 1),\n",
       " (45, 1),\n",
       " (46, 1),\n",
       " (47, 1),\n",
       " (48, 1),\n",
       " (49, 1),\n",
       " (50, 1)]"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corpus[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b68a9eda",
   "metadata": {},
   "source": [
    "id2word= co.Dictionary(data_words)\n",
    "def corpus_d(data_words, id2word):\n",
    "    corpus=[]\n",
    "    i=1\n",
    "    for text in data_words:\n",
    "        print(i)\n",
    "        corpus.append(id2word.doc2bow(text))\n",
    "        i+=1\n",
    "    return corpus\n",
    "\n",
    "\n",
    "corpus= corpus_d(data_words, id2word)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "id": "0f261167",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1000"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(corpus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ae7c1507",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9d8876f7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "15c92b93",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6bf735ea",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "76ac0280",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f9a9f8b2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "id": "47211ee0",
   "metadata": {},
   "outputs": [],
   "source": [
    "lda= gensim.models.ldamodel.LdaModel(corpus=corpus,\n",
    "                                    num_topics=100,\n",
    "                                    id2word=id2word,\n",
    "                                    chunksize=200,\n",
    "                                    passes=10,\n",
    "                                    update_every=1,\n",
    "                                    alpha='auto',\n",
    "                                    random_state=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5eda0c14",
   "metadata": {},
   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8a9aafca",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "id": "e4203c3a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "\n",
       "<div id=\"ldavis_el541628598046823844229282124\"></div>\n",
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\"quarry\", \"quarter\", \"queen\", \"quest\", \"quest\", \"question\", \"question\", \"question\", \"question\", \"question\", \"questionable\", \"queue\", \"quiz\", \"rabbit\", \"race\", \"race\", \"racecar\", \"racer\", \"racetrack\", \"radiation\", \"radiation\", \"radio\", \"radio\", \"radio\", \"radio\", \"raft\", \"rail\", \"rain\", \"raindrop\", \"rally\", \"ranch\", \"random\", \"range\", \"rank\", \"ransom\", \"raptor\", \"rare\", \"rat\", \"reach\", \"read\", \"ready\", \"real\", \"real\", \"real\", \"real\", \"real\", \"real\", \"real\", \"real\", \"reality\", \"realization\", \"realize\", \"realm\", \"realtor\", \"reaping\", \"reappearance\", \"rear\", \"reason\", \"rebel\", \"rebel\", \"rebel\", \"rebellion\", \"rebellion\", \"recall\", \"receive\", \"reception\", \"recipe\", \"recital\", \"reclaim\", \"reconcile\", \"reconciliation\", \"record\", \"record\", \"record\", \"recording\", \"recording\", \"recreational\", \"recruit\", \"rectum\", \"red\", \"red\", \"red\", \"red\", \"redirect\", \"reel\", \"referee\", \"reflect\", \"refrigerator\", \"refuge\", \"refugee\", \"regain\", \"regeneration\", \"regular\", \"reign\", \"relationship\", \"relationship\", \"relationship\", \"relationship\", \"relationship\", \"relative\", \"release\", \"release\", \"reliable\", \"religion\", \"reluctant\", \"remain\", \"remark\", \"remember\", \"remember\", \"remind\", \"remorse\", \"remote\", \"remove\", \"remy\", \"rental\", \"repair\", \"repair\", \"replacement\", \"replica\", \"reply\", \"report\", \"report\", \"report\", \"reporter\", \"reporter\", \"reporter\", \"represent\", \"representative\", \"request\", \"rescue\", \"rescue\", \"rescue\", \"rescue\", \"rescue\", \"research\", \"research\", \"reservation\", \"residence\", \"resist\", \"resistance\", \"resort\", \"respected\", \"responsibility\", \"responsible\", \"rest\", \"rest\", \"restaurant\", \"restaurant\", \"restaurant\", \"restaurant\", \"restaurant\", \"restore\", \"result\", \"retake\", \"retire\", \"retirement\", \"retreat\", \"retribution\", \"retrieve\", \"retrieve\", \"return\", \"return\", \"return\", \"return\", \"return\", \"return\", \"reunite\", \"reveal\", \"reveal\", \"revenge\", \"revenge\", \"revenge\", \"reverse\", \"revert\", \"revert\", \"review\", \"review\", \"revive\", \"revive\", \"rich\", \"riddle\", \"ride\", \"rider\", \"rifle\", \"rifle\", \"rifle\", \"rifle\", \"rifle\", \"rifle\", \"rig\", \"rig\", \"right\", \"right\", \"righteous\", \"rightful\", \"rigorous\", \"ring\", \"ring\", \"ring\", \"ring\", \"ring\", \"ring\", \"rink\", \"ripple\", \"rise\", \"risk\", \"ritual\", \"ritual\", \"rival\", \"rivalry\", \"river\", \"river\", \"river\", \"river\", \"road\", \"roadblock\", \"roar\", \"rob\", \"robber\", \"robbery\", \"robot\", \"robot\", \"robotic\", \"robotic\", \"rock\", \"rocket\", \"rocket\", \"rocket\", \"rocket\", \"rocket\", \"rocky\", \"rod\", \"roll\", \"roman\", \"romantic\", \"romantic\", \"roof\", \"roof\", \"roof\", \"roof\", \"roof\", \"roof\", \"roof\", \"roof\", \"rooftop\", \"rooftop\", \"room\", \"room\", \"room\", \"room\", \"room\", \"room\", \"room\", \"room\", \"room\", \"room\", \"room\", \"room\", \"room\", \"roommate\", \"root\", \"rope\", \"rose\", \"round\", \"round\", \"round\", \"routine\", \"rover\", \"rowboat\", \"royal\", \"rubble\", \"rug\", \"ruin\", \"rule\", \"ruler\", \"rum\", \"rumor\", \"run\", \"russian\", \"russian\", \"ruthless\", \"saber\", \"sacred\", \"sacrifice\", \"sacrifice\", \"sad\", \"safe\", \"safe\", \"safe\", \"safe\", \"safehouse\", \"safety\", \"sail\", \"sail\", \"sailor\", \"sale\", \"sale\", \"saloon\", \"salvation\", \"sample\", \"sample\", \"sand\", \"sand\", \"sand\", \"sand\", \"sand\", \"sarsaparilla\", \"satellite\", \"satellite\", \"satellite\", \"save\", \"save\", \"save\", \"saving\", \"scam\", \"scan\", \"scanner\", \"scar\", \"scare\", \"scarred\", \"scary\", \"scene\", \"scene\", \"scene\", \"scene\", \"scene\", \"scenic\", \"sceptre\", \"scheme\", \"schizophrenia\", \"scholarship\", \"school\", \"school\", \"school\", \"school\", \"school\", \"school\", \"school\", \"school\", \"science\", \"scientific\", \"scientist\", \"scientist\", \"scorn\", \"scottish\", \"scottish\", \"scout\", \"scrap\", \"scream\", \"screen\", \"script\", \"sea\", \"sea\", \"seal\", \"search\", \"search\", \"seaside\", \"season\", \"seat\", \"second\", \"second\", \"second\", \"second\", \"second\", \"second\", \"second\", \"second\", \"second\", \"secret\", \"secret\", \"secret\", \"secret\", \"secret\", \"secret\", \"section\", \"sector\", \"secure\", \"security\", \"security\", \"security\", \"security\", \"seduce\", \"see\", \"see\", \"see\", \"see\", \"see\", \"see\", \"seed\", \"seek\", \"seek\", \"seismic\", \"seizure\", \"seizure\", \"self\", \"self\", \"self\", \"selfless\", \"sell\", \"semen\", \"seminar\", \"senate\", \"senator\", \"send\", \"sensor\", \"sentence\", \"sentient\", \"sentinel\", \"separate\", \"separatist\", \"sergeant\", \"serial\", \"series\", \"series\", \"series\", \"serum\", \"servant\", \"server\", \"service\", \"session\", \"set\", \"setting\", \"settle\", \"settlement\", \"settlement\", \"several\", \"several\", \"several\", \"several\", \"several\", \"severed\", \"sewer\", \"sex\", \"sex\", \"sexual\", \"shack\", \"shadow\", \"shape\", \"share\", \"shark\", \"shaving\", \"shed\", \"sheep\", \"shell\", \"sheriff\", \"sheriff\", \"sheriff\", \"shift\", \"ship\", \"ship\", \"ship\", \"ship\", \"ship\", \"ship\", \"ship\", \"ship\", \"ship\", \"ship\", \"shipwreck\", \"shipyard\", \"shirt\", \"shock\", \"shocker\", \"shoe\", \"shoe\", \"shoot\", \"shoot\", \"shoot\", \"shoot\", \"shoot\", \"shooting\", \"shootout\", \"shootout\", \"shop\", \"shop\", \"shop\", \"shopkeeper\", \"shore\", \"short\", \"short\", \"shot\", \"shot\", \"shot\", \"shot\", \"shotgun\", \"shoulder\", \"show\", \"show\", \"show\", \"show\", \"show\", \"shower\", \"shower\", \"shrapnel\", \"shrink\", \"shut\", \"shuttle\", \"shuttle\", \"shuttlecraft\", \"sick\", \"sick\", \"sickbay\", \"side\", \"side\", \"side\", \"side\", \"side\", \"side\", \"side\", \"sided\", \"sidestep\", \"sight\", \"sign\", \"sign\", \"signal\", \"silent\", \"silent\", \"similar\", \"simulation\", \"simulator\", \"sin\", \"singin\", \"singing\", \"single\", \"sister\", \"sister\", \"sister\", \"sister\", \"sit\", \"sit\", \"site\", \"site\", \"site\", \"site\", \"situation\", \"situation\", \"size\", \"size\", \"skeletal\", \"skeptical\", \"skiff\", \"skill\", \"skill\", \"skull\", \"skull\", \"sky\", \"sky\", \"sky\", \"slaughter\", \"sled\", \"sleep\", \"sleigh\", \"slip\", \"sliver\", \"slum\", \"slump\", \"small\", \"small\", \"small\", \"small\", \"small\", \"smart\", \"smell\", \"smuggle\", \"smuggler\", \"smuggler\", \"smuggling\", \"snake\", \"sniper\", \"snow\", \"snow\", \"snow\", \"snow\", \"snow\", \"sober\", \"sock\", \"solar\", \"soldier\", \"soldier\", \"soldier\", \"soldier\", \"soldier\", \"soldier\", \"soldier\", \"soldier\", \"solitary\", \"solve\", \"someone\", \"someone\", \"something\", \"something\", \"son\", \"son\", \"son\", \"son\", \"son\", \"son\", \"son\", \"son\", \"song\", \"song\", \"soul\", \"sound\", \"sound\", \"soundtrack\", \"soup\", \"sour\", \"south\", \"soviet\", \"soviet\", \"space\", \"space\", \"space\", \"spacecraft\", \"spaceship\", \"spaceship\", \"spacesuit\", \"spatter\", \"spatter\", \"spear\", \"spear\", \"spearhead\", \"special\", \"specie\", \"specimen\", \"spectre\", \"speech\", \"speed\", \"speed\", \"spell\", \"spell\", \"spell\", \"spell\", \"spending\", \"spider\", \"spider\", \"spider\", \"spider\", \"spin\", \"spinner\", \"spinning\", \"splendid\", \"splice\", \"splinter\", \"spoiler\", \"spoon\", \"sport\", \"spot\", \"spot\", \"spy\", \"spy\", \"squad\", \"squad\", \"squadron\", \"square\", \"st\", \"stab\", \"stable\", \"stadium\", \"staff\", \"staff\", \"staff\", \"staff\", \"staff\", \"stag\", \"stage\", \"stage\", \"stair\", \"stair\", \"stairway\", \"stairwell\", \"stakeout\", \"stall\", \"stamp\", \"stampede\", \"stand\", \"star\", \"star\", \"star\", \"stardust\", \"starling\", \"starvation\", \"starve\", \"state\", \"statement\", \"station\", \"station\", \"station\", \"station\", \"station\", \"station\", \"station\", \"statue\", \"statue\", \"statue\", \"statue\", \"statue\", \"status\", \"stay\", \"stay\", \"stay\", \"steady\", \"steal\", \"steal\", \"steal\", \"steel\", \"stepfather\", \"stereo\", \"steroid\", \"stewardess\", \"stick\", \"stitch\", \"stomach\", \"stone\", \"stone\", \"stone\", \"stone\", \"stop\", \"stop\", \"stop\", \"stop\", \"stop\", \"stop\", \"storage\", \"store\", \"store\", \"store\", \"store\", \"store\", \"storefront\", \"storm\", \"storm\", \"storm\", \"storm\", \"story\", \"story\", \"story\", \"story\", \"story\", \"story\", \"storyline\", \"strange\", \"strategic\", \"strategy\", \"stream\", \"street\", \"street\", \"street\", \"strength\", \"strength\", \"stress\", \"strike\", \"strike\", \"strike\", \"string\", \"strip\", \"strip\", \"stripper\", \"stroke\", \"strong\", \"stronghold\", \"structure\", \"strung\", \"stub\", \"student\", \"student\", \"studio\", \"studio\", \"study\", \"stunned\", \"stunt\", \"style\", \"stylist\", \"subject\", \"subjective\", \"submachine\", \"substance\", \"substitute\", \"suburb\", \"subway\", \"subway\", \"success\", \"successful\", \"successor\", \"sugar\", \"suicide\", \"suicide\", \"suit\", \"suit\", \"suit\", \"suit\", \"suit\", \"suit\", \"suit\", \"suit\", \"suit\", \"suitcase\", \"summer\", \"summit\", \"sun\", \"sunlight\", \"super\", \"superhero\", \"superhero\", \"superhuman\", \"superhumanly\", \"superior\", \"supervisor\", \"supplier\", \"supply\", \"supply\", \"supply\", \"supply\", \"supply\", \"supply\", \"support\", \"sure\", \"surface\", \"surfer\", \"surgery\", \"surgery\", \"surprise\", \"surprised\", \"surrender\", \"surrogate\", \"surveillance\", \"survival\", \"survive\", \"survive\", \"survivor\", \"survivor\", \"suspicion\", \"suv\", \"swamp\", \"sweater\", \"swim\", \"switch\", \"switch\", \"sword\", \"sword\", \"sword\", \"sword\", \"sword\", \"sword\", \"sword\", \"sword\", \"symbol\", \"symbol\", \"symbol\", \"synthetic\", \"syringe\", \"system\", \"system\", \"system\", \"system\", \"system\", \"system\", \"system\", \"table\", \"table\", \"tactic\", \"tag\", \"tail\", \"tailor\", \"tale\", \"talent\", \"talk\", \"talk\", \"talkie_walkie\", \"tall\", \"tall\", \"tank\", \"tank\", \"tank\", \"tank\", \"tank\", \"tank\", \"tanker\", \"tape\", \"tape\", \"tar\", \"target\", \"target\", \"tarmac\", \"tarot\", \"task\", \"task\", \"taste\", \"tattoo\", \"tattoo\", \"tax\", \"taxi\", \"taxi\", \"teach\", \"teacher\", \"teacher\", \"teacher\", \"teacher\", \"teaching\", \"team\", \"team\", \"team\", \"team\", \"team\", \"team\", \"team\", \"team\", \"team\", \"team\", \"teammate\", \"tear\", \"technician\", \"technology\", \"technology\", \"teenage\", \"teenager\", \"teenager\", \"telegram\", \"telekinetic\", \"telepathic\", \"telephone\", \"telescope\", \"television\", \"tell\", \"tell\", \"teller\", \"temper\", \"temple\", \"temple\", \"temple\", \"temptation\", \"tentacled\", \"term\", \"term\", \"terminal\", \"terraforming\", \"terrified\", \"terrifying\", \"test\", \"test\", \"test\", \"test\", \"test\", \"test\", \"testify\", \"tether\", \"th\", \"th\", \"th\", \"th\", \"th\", \"th\", \"thank\", \"thaw\", \"theater\", \"theater\", \"theater\", \"theatrical\", \"theft\", \"theme\", \"theological\", \"theoretical\", \"theory\", \"theory\", \"theory\", \"therapist\", \"therapy\", \"thesis\", \"thief\", \"thin\", \"thing\", \"think\", \"third\", \"thirst\", \"thorough\", \"thought\", \"thousand\", \"threat\", \"threat\", \"thrilled\", \"throne\", \"throne\", \"throne\", \"throne\", \"throne\", \"throw\", \"thruster\", \"thug\", \"thug\", \"thug\", \"thwart\", \"thwart\", \"tie\", \"tie\", \"tilde\", \"timeline\", \"timing\", \"tin\", \"tiny\", \"tip\", \"tire\", \"title\", \"title\", \"toe\", \"toilet\", \"toll\", \"tomorrow\", \"tongue\", \"tool\", \"tool\", \"tooth\", \"top\", \"topple\", \"torture\", \"totem\", \"touch\", \"tour\", \"tour\", \"tourist\", \"tower\", \"tower\", \"tower\", \"tower\", \"town\", \"town\", \"town\", \"town\", \"town\", \"townspeople\", \"toxic\", \"toxin\", \"toy\", \"track\", \"tracker\", \"tractor\", \"trade\", \"trader\", \"trading\", \"tradition\", \"traditional\", \"trail\", \"trailer\", \"trailer\", \"trailer\", \"trailer\", \"train\", \"train\", \"train\", \"train\", \"train\", \"train\", \"train\", \"train\", \"train\", \"trainer\", \"training\", \"training\", \"training\", \"traitor\", \"transfer\", \"transform\", \"transformer\", \"transfusion\", \"transfusion\", \"translate\", \"translation\", \"transmission\", \"transmit\", \"transport\", \"trapdoor\", \"trapped\", \"travel\", \"travel\", \"travel\", \"traveler\", \"treachery\", \"treason\", \"treasure\", \"treasure\", \"treasure\", \"treat\", \"treatment\", \"treatment\", \"treatment\", \"trebuchet\", \"tree\", \"tree\", \"tree\", \"trench\", \"trial\", \"tribe\", \"tribute\", \"trick\", \"trickster\", \"trigger\", \"trio\", \"trio\", \"tripod\", \"troll\", \"troop\", \"trooper\", \"troubled\", \"troubling\", \"troupe\", \"truck\", \"truck\", \"truck\", \"truck\", \"trucker\", \"true\", \"true\", \"trust\", \"truth\", \"tube\", \"tunnel\", \"tunnel\", \"tunnel\", \"tunnel\", \"tunnel\", \"turn\", \"turtle\", \"tv\", \"tv\", \"tv\", \"tv\", \"twin\", \"twin\", \"twin\", \"ugly\", \"ultimate\", \"umbrella\", \"unable\", \"unanimous\", \"unbalanced\", \"uncertainty\", \"uncle\", \"uncle\", \"undead\", \"undefeated\", \"undercover\", \"underground\", \"underground\", \"underwear\", \"undisclosed\", \"unemployment\", \"unfeeling\", \"unfinished\", \"unicorn\", \"uniform\", \"uniform\", \"unique\", \"unit\", \"unit\", \"unit\", \"universe\", \"unknown\", \"unleash\", \"unseen\", \"unsettled\", \"unstable\", \"unsuccessful\", \"unwilling\", \"upcoming\", \"upgrade\", \"uplink\", \"upper\", \"uprising\", \"upset\", \"upset\", \"upset\", \"urge\", \"urn\", \"use\", \"use\", \"use\", \"use\", \"use\", \"use\", \"useful\", \"user\", \"utilize\", \"vacant\", \"vacation\", \"vaccine\", \"valiant\", \"valley\", \"valuable\", \"value\", \"vampire\", \"van\", \"van\", \"van\", \"van\", \"van\", \"vandalism\", \"vantage\", \"various\", \"vault\", \"vault\", \"vault\", \"vault\", \"vault\", \"vehicle\", \"vehicle\", \"velociraptor\", \"vendetta\", \"vengeance\", \"vengeance\", \"vengeance\", \"venom\", \"venomous\", \"verdict\", \"version\", \"vessel\", \"vessel\", \"veteran\", \"vial\", \"vial\", \"victim\", \"victim\", \"victor\", \"victory\", \"victory\", \"video\", \"videotape\", \"view\", \"vigilante\", \"viking\", \"village\", \"village\", \"village\", \"village\", \"villager\", \"villain\", \"villain\", \"violence\", \"violent\", \"violent\", \"violin\", \"violin\", \"virologist\", \"virus\", \"virus\", \"visible\", \"vision\", \"vision\", \"vision\", \"vision\", \"vision\", \"vision\", \"vision\", \"voice\", \"voice\", \"voice\", \"voice\", \"volunteer\", \"voodoo\", \"vote\", \"vote\", \"vulnerability\", \"wait\", \"wake\", \"walk\", \"wall\", \"wall\", \"wall\", \"wallet\", \"waltz\", \"wand\", \"want\", \"want\", \"want\", \"war\", \"war\", \"war\", \"war\", \"war\", \"war\", \"war\", \"warden\", \"warden\", \"warehouse\", \"warehouse\", \"warp\", \"warps\", \"warrant\", \"warrior\", \"warrior\", \"warthog\", \"watch\", \"watch\", \"watch\", \"water\", \"water\", \"water\", \"water\", \"water\", \"water\", \"water\", \"wave\", \"wealthy\", \"wealthy\", \"weapon\", \"weapon\", \"weapon\", \"weapon\", \"weapon\", \"weapon\", \"weapon\", \"weaponry\", \"wear\", \"weather\", \"website\", \"wedding\", \"wedding\", \"wedding\", \"wedding\", \"wedding\", \"week\", \"weekend\", \"weight\", \"werewolf\", \"west\", \"wheel\", \"wheelchair\", \"whereabouts\", \"whim\", \"whip\", \"whip\", \"whistle\", \"white\", \"white\", \"white\", \"whole\", \"whole\", \"widow\", \"wield\", \"wife\", \"wife\", \"wife\", \"wife\", \"wife\", \"wife\", \"wife\", \"wife\", \"wig\", \"wild\", \"wildebeest\", \"willing\", \"win\", \"win\", \"win\", \"win\", \"wind\", \"windmill\", \"window\", \"window\", \"window\", \"window\", \"window\", \"window\", \"window\", \"window\", \"windowless\", \"wine\", \"wing\", \"wing\", \"wing\", \"winner\", \"winning\", \"winter\", \"winter\", \"wire\", \"wish\", \"wish\", \"witch\", \"withstand\", \"witness\", \"wizard\", \"wizard\", \"wolf\", \"woman\", \"woman\", \"woman\", \"woman\", \"woman\", \"woman\", \"woman\", \"womanizer\", \"wood\", \"wood\", \"wood\", \"wooden\", \"wooden\", \"word\", \"word\", \"work\", \"work\", \"work\", \"work\", \"worker\", \"worker\", \"worker\", \"workshop\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"worldwide\", \"worldwide\", \"worth\", \"worthy\", \"wound\", \"wound\", \"wreck\", \"wreckage\", \"wrist\", \"wrist\", \"wristwatch\", \"writer\", \"wrong\", \"yacht\", \"yacht\", \"yank\", \"yard\", \"yellow\", \"young\", \"young\", \"young\", \"young\", \"young\", \"youthful\", \"zebra\", \"zip\", \"zombie\", \"zombie\", \"zone\", \"zoo\"]}, \"R\": 30, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [72, 26, 9, 73, 61, 43, 83, 42, 80, 93, 78, 82, 5, 34, 22, 36, 7, 79, 58, 95, 44, 98, 38, 48, 37, 88, 87, 66, 96, 85, 63, 46, 100, 74, 10, 33, 49, 71, 65, 20, 13, 41, 30, 24, 19, 68, 60, 90, 97, 89, 4, 39, 86, 35, 69, 3, 6, 57, 67, 12, 1, 40, 2, 32, 50, 77, 62, 55, 99, 31, 84, 23, 81, 59, 92, 15, 56, 17, 75, 52, 70, 8, 53, 29, 21, 64, 11, 14, 54, 51, 27, 25, 91, 16, 45, 28, 94, 18, 47, 76]};\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_el541628598046823844229282124\", ldavis_el541628598046823844229282124_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://d3js.org/d3.v5\"}});\n",
       "   require([\"d3\"], function(d3){\n",
       "      window.d3 = d3;\n",
       "      LDAvis_load_lib(\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.3.1/pyLDAvis/js/ldavis.v3.0.0.js\", function(){\n",
       "        new LDAvis(\"#\" + \"ldavis_el541628598046823844229282124\", ldavis_el541628598046823844229282124_data);\n",
       "      });\n",
       "    });\n",
       "}else{\n",
       "    // require.js not available: dynamically load d3 & LDAvis\n",
       "    LDAvis_load_lib(\"https://d3js.org/d3.v5.js\", function(){\n",
       "         LDAvis_load_lib(\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.3.1/pyLDAvis/js/ldavis.v3.0.0.js\", function(){\n",
       "                 new LDAvis(\"#\" + \"ldavis_el541628598046823844229282124\", ldavis_el541628598046823844229282124_data);\n",
       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "PreparedData(topic_coordinates=              x         y  topics  cluster      Freq\n",
       "topic                                               \n",
       "71     0.088335  0.500644       1        1  5.031969\n",
       "25     0.344881  0.308441       2        1  2.820927\n",
       "8     -0.186564  0.412300       3        1  2.676086\n",
       "72    -0.051600  0.430649       4        1  2.613827\n",
       "60     0.452334 -0.079350       5        1  2.526333\n",
       "...         ...       ...     ...      ...       ...\n",
       "27     0.004757 -0.016693      96        1  0.028551\n",
       "93     0.004883 -0.014916      97        1  0.026457\n",
       "17     0.005551 -0.015744      98        1  0.017376\n",
       "46     0.005515 -0.015758      99        1  0.017313\n",
       "75     0.005509 -0.015756     100        1  0.017187\n",
       "\n",
       "[100 rows x 5 columns], topic_info=       Term        Freq       Total  Category  logprob  loglift\n",
       "196     car  210.000000  210.000000   Default  30.0000  30.0000\n",
       "276     son  110.000000  110.000000   Default  29.0000  29.0000\n",
       "111  father  195.000000  195.000000   Default  28.0000  28.0000\n",
       "363    ship  112.000000  112.000000   Default  27.0000  27.0000\n",
       "167   house  194.000000  194.000000   Default  26.0000  26.0000\n",
       "..      ...         ...         ...       ...      ...      ...\n",
       "25      oak    0.000663    3.949601  Topic100  -9.4781  -0.0239\n",
       "26   office    0.000663   60.237565  Topic100  -9.4781  -2.7485\n",
       "27    order    0.000663   41.988091  Topic100  -9.4781  -2.3876\n",
       "28   parole    0.000663    5.582484  Topic100  -9.4781  -0.3699\n",
       "29   poster    0.000663    5.370875  Topic100  -9.4781  -0.3312\n",
       "\n",
       "[4345 rows x 6 columns], token_table=      Topic      Freq       Term\n",
       "term                            \n",
       "1768     68  0.778874    abandon\n",
       "2040     45  0.762966    abdomen\n",
       "806      22  0.912888  abduction\n",
       "238       2  0.112496    ability\n",
       "238      16  0.374987    ability\n",
       "...     ...       ...        ...\n",
       "3023      8  0.896852        zip\n",
       "2409     50  0.829290     zombie\n",
       "2409     64  0.103661     zombie\n",
       "4474     50  0.901171       zone\n",
       "4691      3  0.941369        zoo\n",
       "\n",
       "[4857 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[72, 26, 9, 73, 61, 43, 83, 42, 80, 93, 78, 82, 5, 34, 22, 36, 7, 79, 58, 95, 44, 98, 38, 48, 37, 88, 87, 66, 96, 85, 63, 46, 100, 74, 10, 33, 49, 71, 65, 20, 13, 41, 30, 24, 19, 68, 60, 90, 97, 89, 4, 39, 86, 35, 69, 3, 6, 57, 67, 12, 1, 40, 2, 32, 50, 77, 62, 55, 99, 31, 84, 23, 81, 59, 92, 15, 56, 17, 75, 52, 70, 8, 53, 29, 21, 64, 11, 14, 54, 51, 27, 25, 91, 16, 45, 28, 94, 18, 47, 76])"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pyLDAvis.enable_notebook()\n",
    "vis= pyLDAvis.gensim_models.prepare(lda, corpus, id2word, mds='mmds', R=30)\n",
    "vis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "75411fa6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "90a54a97",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "id": "3b2d19d4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "a=vis.token_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "70800173",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9b82490a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "id": "71539c9c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "set()\n"
     ]
    }
   ],
   "source": [
    "print(set(a[a.Topic==71]['Term'].to_list()).intersection(set(movies.keywords[2499].split(' '))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cfd4946f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "id": "b88444f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "i=id2word.doc2bow(movies.synopsis[2499].split(' '))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "id": "76c09200",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(8, 0.07546005),\n",
       " (11, 0.030334707),\n",
       " (33, 0.11285301),\n",
       " (36, 0.03237478),\n",
       " (40, 0.119161926),\n",
       " (41, 0.034414407),\n",
       " (43, 0.04098423),\n",
       " (57, 0.014591547),\n",
       " (67, 0.03059344),\n",
       " (68, 0.059776567),\n",
       " (70, 0.045714457),\n",
       " (71, 0.10409734),\n",
       " (77, 0.0356462),\n",
       " (86, 0.09117066),\n",
       " (92, 0.038422678),\n",
       " (98, 0.030257396)]"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lda.get_document_topics(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7a8b49ba",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f1a504f7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c4ddbd00",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "f8d8c37f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(19,\n",
       "  '0.029*\"canyon\" + 0.027*\"wheel\" + 0.026*\"interested\" + 0.023*\"biker\" + 0.021*\"imminent\" + 0.020*\"rig\" + 0.019*\"mud\" + 0.015*\"lung\" + 0.015*\"dart\" + 0.014*\"replica\"'),\n",
       " (5,\n",
       "  '0.063*\"chinese\" + 0.022*\"fortress\" + 0.022*\"pack\" + 0.022*\"oxygen\" + 0.021*\"loyal\" + 0.020*\"worth\" + 0.019*\"capsule\" + 0.016*\"stronghold\" + 0.015*\"captive\" + 0.015*\"atmosphere\"'),\n",
       " (13,\n",
       "  '0.038*\"reason\" + 0.036*\"baseball\" + 0.024*\"wear\" + 0.022*\"vote\" + 0.021*\"scandal\" + 0.021*\"acid\" + 0.020*\"discussion\" + 0.019*\"stab\" + 0.018*\"national\" + 0.017*\"intention\"'),\n",
       " (31,\n",
       "  '0.052*\"evidence\" + 0.039*\"angry\" + 0.030*\"coma\" + 0.029*\"cake\" + 0.025*\"argument\" + 0.025*\"courthouse\" + 0.023*\"nose\" + 0.021*\"jury\" + 0.021*\"guilty\" + 0.021*\"jacket\"'),\n",
       " (25,\n",
       "  '0.045*\"race\" + 0.044*\"friendship\" + 0.027*\"authority\" + 0.023*\"institution\" + 0.022*\"design\" + 0.020*\"collar\" + 0.020*\"session\" + 0.017*\"quarter\" + 0.017*\"badge\" + 0.016*\"version\"'),\n",
       " (40,\n",
       "  '0.047*\"pregnant\" + 0.035*\"debt\" + 0.025*\"horror\" + 0.023*\"afterlife\" + 0.022*\"convince\" + 0.021*\"obese\" + 0.020*\"gunpoint\" + 0.020*\"stomach\" + 0.018*\"physical\" + 0.018*\"magazine\"'),\n",
       " (49,\n",
       "  '0.078*\"leave\" + 0.033*\"seat\" + 0.031*\"attendant\" + 0.029*\"sale\" + 0.026*\"insurance\" + 0.026*\"attacker\" + 0.026*\"kidnapping\" + 0.024*\"shooting\" + 0.023*\"alternate\" + 0.021*\"loan\"'),\n",
       " (6,\n",
       "  '0.040*\"relationship\" + 0.031*\"company\" + 0.026*\"affair\" + 0.020*\"secret\" + 0.019*\"killer\" + 0.019*\"character\" + 0.016*\"alive\" + 0.015*\"person\" + 0.015*\"lover\" + 0.015*\"duty\"'),\n",
       " (15,\n",
       "  '0.062*\"th\" + 0.040*\"book\" + 0.034*\"son\" + 0.034*\"wood\" + 0.030*\"birthday\" + 0.024*\"boss\" + 0.024*\"ex\" + 0.024*\"college\" + 0.022*\"worker\" + 0.020*\"come\"'),\n",
       " (2,\n",
       "  '0.025*\"leader\" + 0.025*\"pilot\" + 0.024*\"test\" + 0.022*\"son\" + 0.018*\"protect\" + 0.016*\"warrior\" + 0.015*\"television\" + 0.014*\"plane\" + 0.014*\"field\" + 0.013*\"oil\"'),\n",
       " (9,\n",
       "  '0.043*\"ship\" + 0.030*\"crew\" + 0.027*\"team\" + 0.023*\"alien\" + 0.022*\"order\" + 0.022*\"human\" + 0.021*\"mission\" + 0.020*\"machine\" + 0.017*\"planet\" + 0.016*\"device\"'),\n",
       " (46,\n",
       "  '0.049*\"body\" + 0.029*\"attack\" + 0.027*\"town\" + 0.023*\"camp\" + 0.022*\"murder\" + 0.019*\"crime\" + 0.017*\"large\" + 0.016*\"many\" + 0.016*\"horse\" + 0.016*\"cabin\"'),\n",
       " (7,\n",
       "  '0.040*\"phone\" + 0.037*\"apartment\" + 0.030*\"cop\" + 0.029*\"head\" + 0.025*\"help\" + 0.024*\"call\" + 0.023*\"dog\" + 0.019*\"fight\" + 0.019*\"city\" + 0.018*\"doctor\"'),\n",
       " (32,\n",
       "  '0.031*\"father\" + 0.027*\"school\" + 0.026*\"child\" + 0.023*\"wife\" + 0.022*\"party\" + 0.017*\"film\" + 0.016*\"parent\" + 0.016*\"see\" + 0.016*\"work\" + 0.016*\"movie\"'),\n",
       " (35,\n",
       "  '0.023*\"car\" + 0.022*\"house\" + 0.020*\"family\" + 0.020*\"room\" + 0.019*\"mother\" + 0.017*\"police\" + 0.016*\"gun\" + 0.014*\"girl\" + 0.014*\"kill\" + 0.013*\"group\"')]"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(lda.show_topics(num_topics=15, num_words=10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b320bb38",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b92ea5b8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8b91703b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ac2e9914",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "87c3829c",
   "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>titleId</th>\n",
       "      <th>averageRating</th>\n",
       "      <th>numVotes</th>\n",
       "      <th>title</th>\n",
       "      <th>startYear</th>\n",
       "      <th>runtimeMinutes</th>\n",
       "      <th>genres</th>\n",
       "      <th>synopsis</th>\n",
       "      <th>synopsis_allowed_pos</th>\n",
       "      <th>keywords</th>\n",
       "      <th>scores</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tt1375666</td>\n",
       "      <td>8.8</td>\n",
       "      <td>2122723</td>\n",
       "      <td>Inception</td>\n",
       "      <td>2010</td>\n",
       "      <td>148</td>\n",
       "      <td>Action,Adventure,Sci-Fi</td>\n",
       "      <td>A young man, exhausted and delirious, washes u...</td>\n",
       "      <td>young man exhausted delirious beach see young ...</td>\n",
       "      <td>dream,limbo,team,subconscious,projection,kick,...</td>\n",
       "      <td>0.5233624066972737,0.27957745198681444,0.22574...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     titleId  averageRating  numVotes      title  startYear runtimeMinutes  \\\n",
       "2  tt1375666            8.8   2122723  Inception       2010            148   \n",
       "\n",
       "                    genres                                           synopsis  \\\n",
       "2  Action,Adventure,Sci-Fi  A young man, exhausted and delirious, washes u...   \n",
       "\n",
       "                                synopsis_allowed_pos  \\\n",
       "2  young man exhausted delirious beach see young ...   \n",
       "\n",
       "                                            keywords  \\\n",
       "2  dream,limbo,team,subconscious,projection,kick,...   \n",
       "\n",
       "                                              scores  \n",
       "2  0.5233624066972737,0.27957745198681444,0.22574...  "
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies[movies.title=='Inception']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4c15eb39",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c4f06abe",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ece60ae7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c2df8b5a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "911d8501",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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