{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "4591d11d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\", category=DeprecationWarning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3989ae28",
   "metadata": {},
   "outputs": [],
   "source": [
    "plots=pd.read_csv('tagsText.tsv', sep='\\t')\n",
    "plots1=pd.read_csv('tagsText1.tsv', sep='\\t')\n",
    "plots=plots.append(plots1, ignore_index=True)\n",
    "plots1=pd.read_csv('tagsText2.tsv', sep='\\t')\n",
    "plots=plots.append(plots1, ignore_index=True)\n",
    "plots1=pd.read_csv('tagsText3.tsv', sep='\\t')\n",
    "plots=plots.append(plots1, ignore_index=True)\n",
    "plots1=pd.read_csv('tagsText4.tsv', sep='\\t')\n",
    "plots=plots.append(plots1, ignore_index=True)\n",
    "plots1=pd.read_csv('tagsText5.tsv', sep='\\t')\n",
    "plots=plots.append(plots1, ignore_index=True)\n",
    "plots1=pd.read_csv('tagsText6.tsv', sep='\\t')\n",
    "plots=plots.append(plots1, ignore_index=True)\n",
    "del plots1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d286b2dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "plots=pd.DataFrame(plots['0'].unique(), columns=['synopsis'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f5a92614",
   "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>synopsis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>wrongful imprisonment based on the works of st...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>dc comics joker psychopath clown criminal mast...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>dream ambiguous ending subconscious surprise e...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>fighting surprise ending anti establishment in...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>nonlinear timeline overdose drug overdose drug...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9086</th>\n",
       "      <td>hotel concierge mistress money deception new y...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9087</th>\n",
       "      <td>opera lover judge u.s. supreme court litigatio...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9088</th>\n",
       "      <td>missouri raider william quantrill laundry dryi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9089</th>\n",
       "      <td>instrument tuning hungarian contemplative cine...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9090</th>\n",
       "      <td>visual agnosia brother sister relationship reg...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9091 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               synopsis\n",
       "0     wrongful imprisonment based on the works of st...\n",
       "1     dc comics joker psychopath clown criminal mast...\n",
       "2     dream ambiguous ending subconscious surprise e...\n",
       "3     fighting surprise ending anti establishment in...\n",
       "4     nonlinear timeline overdose drug overdose drug...\n",
       "...                                                 ...\n",
       "9086  hotel concierge mistress money deception new y...\n",
       "9087  opera lover judge u.s. supreme court litigatio...\n",
       "9088  missouri raider william quantrill laundry dryi...\n",
       "9089  instrument tuning hungarian contemplative cine...\n",
       "9090  visual agnosia brother sister relationship reg...\n",
       "\n",
       "[9091 rows x 1 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b599f5a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "texts=plots.to_numpy().reshape(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b18bf4d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9091,)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "texts.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "518c702f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a5c33d29",
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.corpus import stopwords\n",
    "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'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "20d3babc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0c68171e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b7a53799",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "import spacy\n",
    "def lemma(texts, allowed_pos=['NOUN',\"ADJ\",'JJ','JJR', 'VB']):\n",
    "    nlp= spacy.load('en_core_web_md', disable=['parser', 'ner'])\n",
    "    text_out=[]\n",
    "    i=1\n",
    "    for text in texts:\n",
    "        try:\n",
    "            \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",
    "            print(i)\n",
    "            i+=1\n",
    "        except:\n",
    "            print('zzzz',str(i))\n",
    "    \n",
    "    return text_out\n",
    "        \n",
    "lemma_texts= lemma(texts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cbf8e9bd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cd9ac856",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9d747382",
   "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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "from gensim.utils import simple_preprocess\n",
    "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",
    "data_words= gen_words(lemma_texts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bc6c640e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7a41b5a8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "db8a46ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "import gensim\n",
    "# Build the bigram and trigram models\n",
    "bigram = gensim.models.Phrases(data_words, min_count=3, threshold=10) # higher threshold fewer phrases.\n",
    "trigram = gensim.models.Phrases(bigram[data_words], threshold=10)  \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": 54,
   "id": "70b03152",
   "metadata": {},
   "outputs": [],
   "source": [
    "from gensim.models import TfidfModel\n",
    "\n",
    "id2word= gensim.corpora.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": null,
   "id": "49b42949",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a798ec03",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "92c42cab",
   "metadata": {},
   "outputs": [],
   "source": [
    "lda= gensim.models.ldamodel.LdaModel(corpus=corpus,\n",
    "                                    num_topics=50,\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": 56,
   "id": "2c26e0c2",
   "metadata": {},
   "outputs": [
    {
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\"stab_death\", \"court\", \"sever_head\", \"spear\", \"skeleton\", \"storm\", \"burn\", \"epic\", \"death\", \"one\", \"church\", \"knife\", \"dead\", \"corpse\", \"kill\", \"pistol\", \"kidnapping\", \"blood_splatter\", \"wheelchair\", \"serial_killer\", \"lose\", \"shotgun\", \"killer\", \"home\", \"nazi\", \"hide\", \"suspense\", \"base_book\", \"wall\", \"end\", \"massacre\", \"person\", \"revolver\", \"miss_person\", \"german\", \"pianist\", \"horse_ride\", \"suitcase\", \"traitor\", \"evil\", \"red\", \"title\", \"teenage\", \"book\", \"family\", \"teenager\", \"secret\", \"high_school\", \"dancing\", \"character_name\", \"band\", \"television\", \"tv_mini_series\", \"couple\", \"bully\", \"time_travel\", \"wine\", \"marriage_proposal\", \"snake\", \"monkey\", \"screen\", \"smoke\", \"elephant\", \"talk_animal\", \"rabbit\", \"group\", \"pizza\", \"frame\", \"day\", \"novel\", \"overall\", \"young_version_character\", \"base\", \"scene\", \"rear\", \"bed\", \"shower\", \"breast\", \"bare_breast\", \"blonde\", \"public\", \"pantie\", \"undress\", \"fake\", \"college\", \"daughter\", \"objectification\", \"topless\", \"sexy\", \"stand\", \"pretend\", \"fellatio\", \"scantily_clothe\", \"virgin\", \"exploitation\", \"mythology\", \"supernatural_horror\", \"dancer\", \"phone\", \"unfaithfulness\", \"vomiting\", \"bad\", \"istanbul_turkey\", \"prostitute\", \"reference\", \"head\", \"airport\", \"art\", \"paint\", \"punch\", \"fbi_federal_bureau_investigation\", \"bank\", \"pantyhose\", \"card\", \"montage\", \"year_year\", \"animal_title\", \"fbi_agent\", \"railway_station\", \"ex_convict\", \"people\", \"opera\", \"base_real_person\", \"happen_epilogue\", \"smoking\", \"character_say\", \"archive_footage\", \"corporate\", \"wind\", \"hope\", \"job_interview\", \"pot_smoking\", \"classical_music\", \"epidemic\", \"year\", \"child\", \"dog\", \"cry\", \"baby\", \"old\", \"village\", \"urination\", \"young\", \"newspaper\", \"orphan\", \"bridge\", 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\"board\", \"boat\", \"body\", \"bodyguard\", \"bomb\", \"bomber\", \"bond\", \"bondage\", \"booby_trap\", \"book\", \"border\", \"bottle\", \"box\", \"boxing\", \"boyfriend_girlfriend_relationship\", \"bra\", \"break\", \"break_fourth_wall\", \"breaking_enter\", \"breast\", \"bride\", \"bridge\", \"brief\", \"briefcase\", \"britain\", \"british\", \"british_history\", \"british_royal_family\", \"bromance\", \"brother\", \"brother_brother_relationship\", \"brother_sister_relationship\", \"brown\", \"bruise\", \"brutal_violence\", \"brutality\", \"bubble\", \"buddhist\", \"bug\", \"building\", \"bull\", \"bullet\", \"bully\", \"burn\", \"burn\", \"burp\", \"bus\", \"business\", \"business_card\", \"businessman\", \"butler\", \"butt\", \"butterfly\", \"buttock\", \"cage\", \"calendar\", \"california\", \"camera\", \"campfire\", \"cancer\", \"candle\", \"cannibal\", \"cape\", \"capitalist\", \"captain\", \"car\", \"car_accident\", \"car_automobile\", \"car_chase\", \"car_crash\", 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\"drug_snort\", \"drug_use\", \"drummer\", \"drunkenness\", \"drunkenness\", \"dual_wield\", \"duel\", \"duo\", \"dwarf\", \"dynamite\", \"dysfunctional_family\", \"dystopia\", \"eagle\", \"earth\", \"eat\", \"eccentric\", \"education\", \"egypt\", \"electric\", \"elephant\", \"elevator\", \"emperor\", \"employee\", \"employer_employee_relationship\", \"end\", \"end_freeze_frame\", \"end_world\", \"energy\", \"england\", \"english\", \"entrepreneur\", \"environment\", \"epic\", \"epic\", \"epidemic\", \"escape\", \"escape\", \"escape\", \"escort\", \"espionage\", \"europe\", \"event\", \"evil\", \"evil\", \"ex\", \"ex_convict\", \"ex_husband_ex_wife\", \"execution\", \"exercise\", \"experiment\", \"explode_building\", \"exploitation\", \"explosion\", \"expose_thigh\", \"extramarital_affair\", \"eye\", \"eyeglass\", \"face\", \"face_slap\", \"factory\", \"failure\", \"faint\", \"fake\", \"fake_death\", \"fake_documentary\", \"fall_love\", \"false\", \"false_accusation\", \"false_identity\", \"family\", \"family_photograph\", \"family_relationship\", \"fan\", \"fantasy\", \"fantasy_sequence\", \"farm\", \"farmer\", \"fascist\", \"fat\", \"father\", \"father_daughter_relationship\", \"father_figure\", \"father_son\", \"father_son_estrangement\", \"father_son_relationship\", \"father_son_reunion\", \"fbi_agent\", \"fbi_federal_bureau_investigation\", \"fear\", \"fellatio\", \"femme_fatale\", \"fictional\", \"fictional_city\", \"fight\", \"fight\", \"fight_death\", \"fight_system\", \"film\", \"film_within_film\", \"finger_cut\", \"fire\", \"fire_extinguisher\", \"fire_job\", \"firearm\", \"firearm\", \"firework\", \"fish\", \"fishing\", \"five_word_title\", \"flashback\", \"flirt\", \"flood\", \"flower\", \"fly\", \"food\", \"foot\", \"foot_chase\", \"football\", \"force\", \"force_strip\", \"forest\", \"forgery\", \"former\", \"frame\", \"frame_murder\", \"frame_photograph\", \"france\", \"fraud\", \"freeze_frame\", \"french\", \"friend\", \"friend_live_together\", \"friendship\", \"front_child\", \"frustration\", \"fugitive\", \"full\", \"full_rear\", \"funeral\", \"funeral_pyre\", \"fur_coat\", \"future\", \"future\", \"futuristic\", \"gambling\", \"game\", \"gang\", \"gang_war\", \"gangster\", \"garage\", \"garden\", \"gas_mask\", \"gas_station\", \"gay\", \"gay_interest\", \"gay_slur\", \"general\", \"german\", \"ghost\", \"giant\", \"giant_creature\", \"giant_monster\", \"gift\", \"glass\", \"glove\", \"go\", \"god\", \"gold\", \"golf\", \"good\", \"good_friend\", \"good_versus_evil\", \"gordon\", \"gordon_character\", \"gore\", \"gotham_city\", \"government\", \"government_agent\", \"government_conspiracy\", \"grave\", \"great\", \"gross_comedy\", \"group\", \"group_friend\", \"group_shower\", \"grow\", \"guard\", \"guide\", \"guitar\", \"gun\", \"gun_battle\", \"gun_fu\", \"gunfight\", \"gunfire\", \"gunshot\", \"hacker\", \"haircut\", \"half_human\", \"hallucination\", \"hammer\", \"hand\", \"hand_grenade\", \"hand_hand_combat\", \"handgun\", \"handheld_weapon\", \"handshake\", \"happen_epilogue\", \"happy_end\", \"hat\", \"hate\", \"hdtv\", \"head\", \"health\", \"heart_attack\", \"heavy_rain\", \"heist\", \"helicopter\", \"helmet\", \"henchman\", \"hero\", \"hide\", \"high_school\", \"hill\", \"hippie\", \"historical_fiction\", \"historically_inaccurate\", \"history\", \"hit\", \"hit_car\", \"hit_head\", \"hitman\", \"hold_gunpoint\", \"hole\", \"holiday\", \"holocaust\", \"hologram\", \"home\", \"home_invasion\", \"homeless\", \"homicide\", \"homosexual\", \"hope\", \"horn\", \"horse\", \"horse_ride\", \"horseback_ride\", \"hospital\", \"hostage\", \"hot_leg\", \"hotel\", \"hotel_room\", \"house\", \"hug\", \"human\", \"human_animal\", \"human_versus\", \"humiliation\", \"hunter\", \"husband\", \"husband_wife_relationship\", \"hypodermic_needle\", \"ice\", \"ice_cream\", \"imaginary_friend\", \"imitation\", \"immortality\", \"imperialism\", \"impersonation\", \"imply\", \"impostor\", 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\"kissing\", \"kitchen\", \"knife\", \"knock\", \"kung_fu\", \"laboratory\", \"lake\", \"land\", \"landlady\", \"laptop\", \"last_stand\", \"latex_glove\", \"laugh\", \"laughter\", \"law\", \"law_student\", \"lawnmower\", \"lawyer\", \"leader\", \"leg\", \"legal\", \"legend\", \"lesbian\", \"lesbian_kiss\", \"lesson\", \"letter\", \"liar\", \"library\", \"lie\", \"life\", \"light\", \"lightning\", \"like\", \"limp\", \"line\", \"lingerie\", \"listen_music\", \"listen_radio\", \"little\", \"live\", \"lizard\", \"london\", \"london_england\", \"long\", \"long_haired\", \"long_take\", \"look_oneself_mirror\", \"los_angele_california\", \"los_angeles_california\", \"lose\", \"loss_family\", \"loss_love_one\", \"love\", \"love_interest\", \"love_triangle\", \"loyalty\", \"lust\", \"mace\", \"machine_gun\", \"madness\", \"mafia\", \"magic\", \"magician\", \"maid\", \"make\", \"manhattan_new_york_city\", \"manhunt\", \"mansion\", \"map\", \"marine\", \"maroon\", \"marriage\", \"marriage_proposal\", \"martial_art\", \"mask\", \"mass_grave\", \"massacre\", \"master\", \"masturbation\", \"match\", \"medical\", \"medical_examination\", \"meeting\", \"megalomaniac\", \"member\", \"memory\", \"mentor\", \"metal\", \"metropolis_city\", \"mexico\", \"miami_florida\", \"midlife_crisis\", \"military\", \"military_officer\", \"millionaire\", \"mind\", \"mind_control\", \"mini_skirt\", \"mirror\", \"miss\", \"miss_child\", \"miss_person\", \"missile\", \"mississippi\", \"mistaken_identity\", \"mob\", \"mockumentary\", \"money\", \"money\", \"monkey\", \"monster\", \"montage\", \"morality\", \"motel\", \"mother\", \"mother_daughter_relationship\", \"mother_son_relationship\", \"motor_vehicle\", \"motorcycle\", \"mountain\", \"mourning\", \"mouth\", \"move\", \"movie\", \"movie_theater\", \"multiple_cameo\", \"multiple_perspective\", \"multiple_storyline\", \"murder\", \"murder_child\", \"murder_investigation\", \"murder_police_officer\", \"murder_police_officer\", \"murder_spree\", \"murder_wife\", \"murderess\", \"museum\", \"music\", \"musician\", \"musician\", \"mustache\", \"mutation\", \"mythology\", \"name_title\", \"nanny\", \"nasa\", \"national_film_registry\", \"native_american\", \"nature\", \"nazi\", \"nazi_germany\", \"nazism\", \"near_future\", \"neck\", \"neck_break\", \"necklace\", \"neighbor\", \"neo_nazi\", \"neo_noir\", \"nerd\", \"netflix_original\", \"new\", \"new_year_eve\", \"new_york\", \"new_york_city\", \"news_report\", \"newspaper\", \"newspaper_editor\", \"nickname\", \"night\", \"night_time\", \"nightclub\", \"nightmare\", \"nihilism\", \"ninja\", \"nipple_visible_clothing\", \"notebook\", \"novel\", \"nuclear\", \"number_sequel\", \"nurse\", \"objectification\", \"obscene_finger_gesture\", \"obsession\", \"obstacle_course\", \"occupation\", \"octopus\", \"odyssey\", \"office\", \"old\", \"old_young_relationship\", \"one\", \"one_army\", \"one_army\", \"one_many\", \"one_many\", \"one_night\", \"one_word_series_title\", \"one_word_title\", \"opening_action_scene\", \"opera\", \"oral\", \"orchestral_music\", \"order\", \"organize_crime\", \"orphan\", \"outer_space\", \"overall\", \"overturn_car\", \"pacific_ocean\", \"pain\", \"paint\", \"panic\", \"pantie\", \"pantie_pull\", \"pantie_white\", \"pantyhose\", \"parade\", \"paranoia\", \"parent\", \"parenthood\", \"parking_garage\", \"part\", \"partner\", \"party\", \"pass\", \"patient\", \"patricide\", \"pay_phone\", \"people\", \"performance\", \"period_drama\", \"period_piece\", \"person\", \"philosophy\", \"phone\", \"photo\", \"photograph\", \"photographer\", \"physicist\", \"pianist\", \"piano\", \"pickup_truck\", \"pig\", \"pill\", \"pilot\", \"pipe\", \"pistol\", \"pizza\", \"pizza_delivery\", \"plague\", \"plan\", \"planet\", \"plant\", \"play\", \"plot_twist\", \"poker\", \"police\", \"police_brutality\", \"police_captain\", \"police_corruption\", \"police_detective\", \"police_inspector\", \"police_officer\", \"police_officer_kill\", \"police_officer_police_officer\", \"police_station\", \"politic\", \"political\", \"politician\", \"pool_blood\", \"portrait\", \"possession\", \"post\", \"post_apocalypse\", \"pot_smoking\", \"poverty\", \"power\", \"prayer\", \"pregnancy\", \"pregnant\", \"premarital\", \"president\", \"pretend\", \"priest\", \"prince\", \"prison\", \"prison_cell\", \"prison_guard\", \"prisoner\", \"private_detective\", \"private_investigator\", \"product_placement\", \"professional\", \"professor\", \"promise\", \"propaganda\", \"prophecy\", \"prostitute\", \"prostitute\", \"prostitution\", \"protagonist\", \"protective\", \"protest\", \"psychedelic\", \"psychiatrist\", \"psychiatrist\", \"psychic\", \"psychopath\", \"psychopath\", \"psychotronic_film\", \"psychotronic_film\", \"psychotronic_film\", \"pub\", \"public\", \"punch\", \"punch_face\", \"punishment\", \"quarantine\", \"queen\", \"queen_elizabeth\", \"quotation\", \"rabbit\", \"raccoon\", \"race\", \"race_time\", \"racetrack\", \"racial_stereotype\", \"racism\", \"radiation\", \"radio\", \"railway_station\", \"rain\", \"rainstorm\", \"rape\", \"rape_victim\", \"rat\", \"rate\", \"ray\", \"reality\", \"rear\", \"rebel\", \"rebellion\", \"record_player\", \"red\", \"red\", \"red_dress\", \"red_wine\", \"redemption\", \"redheaded\", \"reference\", \"reference_abraham_lincoln\", \"reference_adolf_hitler\", \"reference_david\", \"reference_god\", \"reference_new_york\", \"reference_paul\", \"reference_william_shakespeare\", \"reflection\", \"refrigerator\", \"refugee\", \"regret\", \"rehearsal\", \"relationship\", \"relic\", \"religion\", \"religious\", \"remake\", \"remove\", \"remove_clothe\", \"reporter\", \"rescue\", \"rescue_mission\", \"resistance\", \"resort\", \"restaurant\", \"retirement\", \"revelation\", \"revenge\", \"revolution\", \"revolver\", \"ride\", \"right\", \"ring\", \"rise_fall\", \"ritual\", \"rival\", \"river\", \"road\", \"road_trip\", \"robbery\", \"robbery\", \"robot\", 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\"south\", \"south_korea\", \"southern\", \"space\", \"space_opera\", \"space_shuttle\", \"space_travel\", \"spaceship\", \"spain\", \"speak\", \"speak_character\", \"spear\", \"speech\", \"spider\", \"spirit\", \"spoof\", \"sport\", \"spy\", \"stab\", \"stab_back\", \"stab_chest\", \"stab_death\", \"stab_eye\", \"stab_stomach\", \"stabbing\", \"staff\", \"stage\", \"stalk\", \"stalker\", \"stand\", \"stand_comedian\", \"star_appear\", \"star_war\", \"start_narration\", \"station\", \"station_wagon\", \"statue\", \"stepfather_stepdaughter_relationship\", \"stick\", \"stomp\", \"stone\", \"storm\", \"story\", \"street\", \"street_shootout\", \"strike\", \"strong\", \"strong_character\", \"struggle\", \"student\", \"subjective_camera\", \"subtitle_scene\", \"suburb\", \"subway\", \"suicide\", \"suit_tie\", \"suitcase\", \"sunbathe\", \"sunglass\", \"super\", \"superhero\", \"superhero_base_comic\", \"supermarket\", \"supernatural_horror\", \"supernatural_power\", \"supernatural_power\", \"surfboard\", \"surprise_end\", \"surrealism\", \"surveillance\", \"survival\", \"suspense\", \"suspicion\", \"swallow\", \"swim_pool\", \"swimming_pool\", \"switchblade\", \"sword\", \"sword_fight\", \"sword_fight\", \"syringe\", \"take\", \"talk\", \"talk\", \"talk_animal\", \"talk_camera\", \"tank\", \"tank_top\", \"tattoo\", \"taxi\", \"teacher\", \"teacher_student_relationship\", \"team\", \"teddy_bear\", \"teenage\", \"teenage_hero\", \"teenage_teenage\", \"teenager\", \"telephone\", \"telephone_booth\", \"telephone_call\", \"telescope\", \"television\", \"television_news\", \"terrorism\", \"terrorist\", \"terrorist_attack\", \"test\", \"texas\", \"th_century\", \"theater\", \"therapist\", \"thief\", \"thigh\", \"threat\", \"threaten_knife\", \"three_word_title\", \"thunderstorm\", \"tiger\", \"time_travel\", \"tinnitus\", \"title\", \"title_book\", \"title_speak_character\", \"tomb\", \"tongue\", \"tooth\", \"topless\", \"torture\", \"tough_guy\", \"tournament\", \"tower\", \"toy\", \"tragic_end\", \"trailer\", \"train\", \"train_station\", \"training\", \"traitor\", \"transformation\", \"translator\", \"trap\", \"trauma\", \"travel\", \"tree\", \"trial\", \"tribal\", \"tribe\", \"triple_rate\", \"trophy\", \"tropical_island\", \"truck\", \"true_love\", \"truth\", \"try\", \"tv\", \"tv_mini_series\", \"tv_news\", \"tv_series\", \"tv_show\", \"twin\", \"twin_brother\", \"twin_sister\", \"two\", \"two_army\", \"two_word_title\", \"typewriter\", \"tyrannosaurus_rex\", \"uh_blackhawk_helicopter\", \"umbrella\", \"uncle_nephew_relationship\", \"undercover\", \"underground\", \"underwater\", \"underwater_scene\", \"undress\", \"unfaithfulness\", \"uniform\", \"united_state\", \"university\", \"urban_set\", \"urination\", \"usa\", \"use\", \"vampire\", \"van\", \"vandalism\", \"vehicle\", \"vengeance\", \"versus\", \"vest\", \"veteran\", \"victim\", \"video_game\", \"vietnam\", \"vietnam_war\", \"vietnam_war_veteran\", \"village\", \"villain\", \"villain\", 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      ],
      "text/plain": [
       "PreparedData(topic_coordinates=              x         y  topics  cluster      Freq\n",
       "topic                                               \n",
       "32     0.188753 -0.514125       1        1  7.910258\n",
       "0      0.182742  0.498474       2        1  6.363500\n",
       "29    -0.543180  0.010678       3        1  5.877946\n",
       "24     0.318302  0.408281       4        1  5.024137\n",
       "23    -0.291249  0.434241       5        1  4.746581\n",
       "15    -0.093375  0.505949       6        1  4.499517\n",
       "2      0.349943 -0.375934       7        1  3.983277\n",
       "1      0.462400 -0.202103       8        1  3.973343\n",
       "10     0.491620  0.050667       9        1  3.706104\n",
       "43     0.398307  0.278813      10        1  3.474966\n",
       "11     0.481894 -0.066291      11        1  3.436171\n",
       "35     0.448044  0.166532      12        1  3.121995\n",
       "20    -0.457018  0.214501      13        1  3.089331\n",
       "17    -0.452783 -0.195662      14        1  2.572147\n",
       "14    -0.003822 -0.472601      15        1  2.028174\n",
       "25    -0.244572 -0.411852      16        1  2.002232\n",
       "9     -0.351896 -0.304908      17        1  1.777650\n",
       "5     -0.045131  0.366805      18        1  1.775183\n",
       "34     0.041716  0.424241      19        1  1.701986\n",
       "48     0.137027 -0.385802      20        1  1.592371\n",
       "13     0.348114 -0.206168      21        1  1.580544\n",
       "12    -0.154466  0.349084      22        1  1.507516\n",
       "4      0.246824  0.252919      23        1  1.467070\n",
       "26    -0.335136  0.230147      24        1  1.276141\n",
       "42     0.113744  0.310341      25        1  1.227458\n",
       "28    -0.253272  0.269987      26        1  1.208248\n",
       "33    -0.397024 -0.010393      27        1  1.203802\n",
       "21    -0.340539  0.094197      28        1  1.187051\n",
       "46     0.309370  0.044609      29        1  1.152547\n",
       "37    -0.144435 -0.375288      30        1  1.024431\n",
       "41     0.217230 -0.077295      31        1  0.938911\n",
       "30     0.066418  0.206326      32        1  0.938729\n",
       "7      0.202910  0.138899      33        1  0.933050\n",
       "39    -0.325896 -0.103207      34        1  0.920826\n",
       "22     0.170172 -0.271119      35        1  0.906680\n",
       "16    -0.075346 -0.317622      36        1  0.869314\n",
       "45    -0.237316 -0.241001      37        1  0.837095\n",
       "19     0.218933 -0.172545      38        1  0.827773\n",
       "18    -0.141841  0.185521      39        1  0.792768\n",
       "27     0.019932 -0.290418      40        1  0.754116\n",
       "31    -0.209191  0.031428      41        1  0.673372\n",
       "8     -0.047550 -0.205225      42        1  0.650369\n",
       "47     0.160068  0.018035      43        1  0.646881\n",
       "3     -0.027891  0.038993      44        1  0.642535\n",
       "40     0.071793 -0.136435      45        1  0.641826\n",
       "38    -0.203571 -0.163447      46        1  0.611579\n",
       "49    -0.096098  0.079315      47        1  0.531552\n",
       "44     0.052171  0.027458      48        1  0.500696\n",
       "6     -0.164747 -0.072430      49        1  0.478902\n",
       "36    -0.061083 -0.064569      50        1  0.411347, topic_info=                        Term         Freq        Total Category  logprob  \\\n",
       "42                 reference  6574.000000  6574.000000  Default  30.0000   \n",
       "76                 character  4352.000000  4352.000000  Default  29.0000   \n",
       "273                   murder  3527.000000  3527.000000  Default  28.0000   \n",
       "1761            relationship  2926.000000  2926.000000  Default  27.0000   \n",
       "144                    child  2256.000000  2256.000000  Default  26.0000   \n",
       "...                      ...          ...          ...      ...      ...   \n",
       "19                  hayworth     0.026235     1.582120  Topic50 -11.3475   \n",
       "0                    assault     0.026235   159.224638  Topic50 -11.3475   \n",
       "1                     banker     0.026235    70.764970  Topic50 -11.3475   \n",
       "2               base_novella     0.026235     8.557355  Topic50 -11.3475   \n",
       "3     base_work_stephen_king     0.026235    19.051741  Topic50 -11.3475   \n",
       "\n",
       "      loglift  \n",
       "42    30.0000  \n",
       "76    29.0000  \n",
       "273   28.0000  \n",
       "1761  27.0000  \n",
       "144   26.0000  \n",
       "...       ...  \n",
       "19     1.3941  \n",
       "0     -3.2175  \n",
       "1     -2.4065  \n",
       "2     -0.2940  \n",
       "3     -1.0943  \n",
       "\n",
       "[1586 rows x 6 columns], token_table=      Topic      Freq                     Term\n",
       "term                                          \n",
       "6006     40  0.987848                  abandon\n",
       "3693     30  0.987503            absurd_comedy\n",
       "1379     28  0.995394                    abuse\n",
       "352      39  0.974954              abuse_power\n",
       "4315     14  0.995421                 accident\n",
       "...     ...       ...                      ...\n",
       "1645      6  0.018749  young_version_character\n",
       "1645     10  0.974947  young_version_character\n",
       "5293     41  0.967003                   yuppie\n",
       "4377     27  0.995072                   zombie\n",
       "4505     34  0.978119                      zoo\n",
       "\n",
       "[1590 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[33, 1, 30, 25, 24, 16, 3, 2, 11, 44, 12, 36, 21, 18, 15, 26, 10, 6, 35, 49, 14, 13, 5, 27, 43, 29, 34, 22, 47, 38, 42, 31, 8, 40, 23, 17, 46, 20, 19, 28, 32, 9, 48, 4, 41, 39, 50, 45, 7, 37])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pyLDAvis\n",
    "import pyLDAvis.gensim_models\n",
    "pyLDAvis.enable_notebook()\n",
    "vis= pyLDAvis.gensim_models.prepare(lda, corpus, id2word, mds='mmds', R=30)\n",
    "vis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "fb59d498",
   "metadata": {},
   "outputs": [],
   "source": [
    "a=lda.show_topics(num_topics=50, num_words=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "ee2dc7b5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0, '0.035*\"flashback\" + 0.033*\"bare_chested\" + 0.029*\"suicide\" + 0.025*\"train\" + 0.025*\"hospital\"')\n",
      "(1, '0.044*\"two_word_title\" + 0.034*\"beach\" + 0.034*\"boat\" + 0.034*\"rain\" + 0.030*\"th_century\"')\n",
      "(2, '0.056*\"new_york_city\" + 0.046*\"party\" + 0.046*\"marriage\" + 0.036*\"photograph\" + 0.035*\"city\"')\n",
      "(3, '0.061*\"period_drama\" + 0.059*\"speech\" + 0.058*\"subjective_camera\" + 0.053*\"shot\" + 0.045*\"set\"')\n",
      "(4, '0.070*\"monster\" + 0.056*\"helicopter\" + 0.054*\"scientist\" + 0.035*\"foot_chase\" + 0.030*\"witch\"')\n",
      "(5, '0.066*\"pregnancy\" + 0.063*\"mother\" + 0.060*\"come\" + 0.052*\"california\" + 0.050*\"dysfunctional_family\"')\n",
      "(6, '0.099*\"farm\" + 0.067*\"cult\" + 0.050*\"duel\" + 0.044*\"colonel\" + 0.039*\"cruise\"')\n",
      "(7, '0.038*\"shoot_back\" + 0.027*\"india\" + 0.026*\"fight_death\" + 0.026*\"bet\" + 0.025*\"garage\"')\n",
      "(8, '0.121*\"island\" + 0.073*\"husband\" + 0.058*\"sister\" + 0.033*\"uncle_nephew_relationship\" + 0.033*\"experiment\"')\n",
      "(9, '0.069*\"drug\" + 0.058*\"hit\" + 0.040*\"journalist\" + 0.038*\"bus\" + 0.032*\"eye\"')\n",
      "(10, '0.041*\"one\" + 0.035*\"evil\" + 0.033*\"church\" + 0.032*\"kill\" + 0.029*\"knife\"')\n",
      "(11, '0.076*\"scene\" + 0.048*\"rear\" + 0.039*\"bed\" + 0.038*\"shower\" + 0.036*\"breast\"')\n",
      "(12, '0.036*\"magic\" + 0.029*\"class_difference\" + 0.027*\"castle\" + 0.026*\"gold\" + 0.026*\"statue\"')\n",
      "(13, '0.062*\"gangster\" + 0.059*\"beat\" + 0.048*\"gang\" + 0.034*\"machine_gun\" + 0.033*\"bomb\"')\n",
      "(14, '0.067*\"horse\" + 0.051*\"snow\" + 0.038*\"forest\" + 0.031*\"river\" + 0.030*\"dress\"')\n",
      "(15, '0.179*\"character\" + 0.044*\"fight\" + 0.026*\"movie\" + 0.026*\"hero\" + 0.025*\"battle\"')\n",
      "(16, '0.043*\"anime\" + 0.038*\"job\" + 0.037*\"sunglass\" + 0.037*\"conflict\" + 0.034*\"horseback_ride\"')\n",
      "(17, '0.052*\"american\" + 0.045*\"funeral\" + 0.037*\"british\" + 0.034*\"london_england\" + 0.034*\"priest\"')\n",
      "(18, '0.096*\"independent_film\" + 0.073*\"police_station\" + 0.042*\"suspicion\" + 0.037*\"coffee\" + 0.033*\"fishing\"')\n",
      "(19, '0.121*\"animal\" + 0.071*\"bird\" + 0.053*\"light\" + 0.048*\"fish\" + 0.046*\"doll\"')\n",
      "(20, '0.135*\"child\" + 0.104*\"dog\" + 0.044*\"cry\" + 0.043*\"baby\" + 0.037*\"old\"')\n",
      "(21, '0.076*\"series_title\" + 0.039*\"abuse\" + 0.034*\"sheriff\" + 0.034*\"execution\" + 0.027*\"murder_investigation\"')\n",
      "(22, '0.089*\"wear\" + 0.058*\"conspiracy\" + 0.050*\"kung_fu\" + 0.035*\"agent\" + 0.032*\"government\"')\n",
      "(23, '0.070*\"car\" + 0.041*\"police_officer\" + 0.037*\"fire\" + 0.036*\"explosion\" + 0.032*\"chase\"')\n",
      "(24, '0.130*\"murder\" + 0.071*\"violence\" + 0.064*\"police\" + 0.054*\"blood\" + 0.052*\"death\"')\n",
      "(25, '0.037*\"nightmare\" + 0.037*\"radio\" + 0.036*\"face\" + 0.035*\"body\" + 0.032*\"ghost\"')\n",
      "(26, '0.101*\"military\" + 0.077*\"soldier\" + 0.059*\"war\" + 0.053*\"army\" + 0.045*\"world_war_two\"')\n",
      "(27, '0.106*\"satire\" + 0.085*\"spoof\" + 0.068*\"slapstick_comedy\" + 0.047*\"nuclear\" + 0.032*\"god\"')\n",
      "(28, '0.078*\"lesbian\" + 0.078*\"full\" + 0.056*\"masturbation\" + 0.043*\"eat\" + 0.040*\"lust\"')\n",
      "(29, '0.092*\"relationship\" + 0.053*\"husband_wife_relationship\" + 0.051*\"kiss\" + 0.051*\"love\" + 0.048*\"friendship\"')\n",
      "(30, '0.065*\"robot\" + 0.065*\"space\" + 0.065*\"alien\" + 0.037*\"outer_space\" + 0.036*\"widower\"')\n",
      "(31, '0.065*\"motor_vehicle\" + 0.063*\"fall_love\" + 0.055*\"vehicle\" + 0.034*\"long_haired\" + 0.033*\"versus\"')\n",
      "(32, '0.055*\"protagonist\" + 0.043*\"death\" + 0.033*\"father_daughter_relationship\" + 0.029*\"one_word_title\" + 0.028*\"character_name_title\"')\n",
      "(33, '0.073*\"student\" + 0.072*\"love_triangle\" + 0.055*\"game\" + 0.054*\"professor\" + 0.044*\"zombie\"')\n",
      "(34, '0.084*\"prison\" + 0.081*\"gay\" + 0.047*\"actor\" + 0.046*\"play\" + 0.042*\"arrest\"')\n",
      "(35, '0.389*\"reference\" + 0.091*\"year\" + 0.025*\"head\" + 0.024*\"airport\" + 0.021*\"art\"')\n",
      "(36, '0.112*\"hand\" + 0.094*\"mansion\" + 0.050*\"farmer\" + 0.045*\"millionaire\" + 0.036*\"butler\"')\n",
      "(37, '0.074*\"letter\" + 0.063*\"comedy\" + 0.054*\"name_title\" + 0.044*\"birthday\" + 0.037*\"subway\"')\n",
      "(38, '0.143*\"rape\" + 0.108*\"politic\" + 0.107*\"son\" + 0.070*\"jail\" + 0.063*\"politician\"')\n",
      "(39, '0.059*\"religion\" + 0.045*\"love_interest\" + 0.044*\"mountain\" + 0.041*\"ice\" + 0.035*\"queen\"')\n",
      "(40, '0.069*\"hat\" + 0.067*\"rooftop\" + 0.065*\"office\" + 0.059*\"vampire\" + 0.051*\"terrorism\"')\n",
      "(41, '0.075*\"nurse\" + 0.074*\"remake\" + 0.074*\"lie\" + 0.046*\"blackmail\" + 0.045*\"japan\"')\n",
      "(42, '0.052*\"history\" + 0.050*\"actress\" + 0.034*\"tv\" + 0.033*\"prayer\" + 0.032*\"pig\"')\n",
      "(43, '0.107*\"title\" + 0.083*\"teenage\" + 0.037*\"book\" + 0.034*\"family\" + 0.034*\"teenager\"')\n",
      "(44, '0.076*\"hit_car\" + 0.067*\"ex\" + 0.065*\"two\" + 0.061*\"humiliation\" + 0.050*\"punishment\"')\n",
      "(45, '0.072*\"cover\" + 0.066*\"bicycle\" + 0.059*\"russian\" + 0.050*\"president\" + 0.043*\"propaganda\"')\n",
      "(46, '0.070*\"nightclub\" + 0.063*\"singing\" + 0.045*\"dinner\" + 0.039*\"factory\" + 0.034*\"leg\"')\n",
      "(47, '0.061*\"show\" + 0.053*\"coffin\" + 0.048*\"full_rear\" + 0.046*\"english\" + 0.045*\"door\"')\n",
      "(48, '0.053*\"criminal\" + 0.046*\"brother\" + 0.038*\"self\" + 0.036*\"thief\" + 0.036*\"scream\"')\n",
      "(49, '0.096*\"japanese\" + 0.083*\"walk\" + 0.064*\"convertible\" + 0.049*\"eyeglass\" + 0.027*\"escort\"')\n"
     ]
    }
   ],
   "source": [
    "for i in a:\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "001147cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c541b8b0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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