Topic Modeling using Gensim and LDA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pprint import pprint\n",
    "\n",
    "# Gensim\n",
    "import gensim\n",
    "import gensim.corpora as corpora\n",
    "from gensim.utils import simple_preprocess\n",
    "from gensim.models import CoherenceModel\n",
    "\n",
    "# spacy for lemmatization\n",
    "import spacy\n",
    "\n",
    "# Plotting tools\n",
    "import pyLDAvis\n",
    "import pyLDAvis.gensim  # don't skip this\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# Enable logging for gensim - optional\n",
    "import logging\n",
    "logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.ERROR)\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\",category=DeprecationWarning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# NLTK Stop words\n",
    "from nltk.corpus import stopwords\n",
    "stop_words = stopwords.words('english')\n",
    "stop_words.extend(['from', 'subject', 're', 'edu', 'use'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['rec.autos' 'comp.sys.mac.hardware' 'comp.graphics' 'sci.space'\n",
      " 'talk.politics.guns' 'sci.med' 'comp.sys.ibm.pc.hardware'\n",
      " 'comp.os.ms-windows.misc' 'rec.motorcycles' 'talk.religion.misc'\n",
      " 'misc.forsale' 'alt.atheism' 'sci.electronics' 'comp.windows.x'\n",
      " 'rec.sport.hockey' 'rec.sport.baseball' 'soc.religion.christian'\n",
      " 'talk.politics.mideast' 'talk.politics.misc' 'sci.crypt']\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "        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>content</th>\n",
       "      <th>target</th>\n",
       "      <th>target_names</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>From: lerxst@wam.umd.edu (where's my thing)\\nS...</td>\n",
       "      <td>7</td>\n",
       "      <td>rec.autos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>From: guykuo@carson.u.washington.edu (Guy Kuo)...</td>\n",
       "      <td>4</td>\n",
       "      <td>comp.sys.mac.hardware</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>From: twillis@ec.ecn.purdue.edu (Thomas E Will...</td>\n",
       "      <td>4</td>\n",
       "      <td>comp.sys.mac.hardware</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>From: jgreen@amber (Joe Green)\\nSubject: Re: W...</td>\n",
       "      <td>1</td>\n",
       "      <td>comp.graphics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>From: jcm@head-cfa.harvard.edu (Jonathan McDow...</td>\n",
       "      <td>14</td>\n",
       "      <td>sci.space</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             content  target  \\\n",
       "0  From: lerxst@wam.umd.edu (where's my thing)\\nS...       7   \n",
       "1  From: guykuo@carson.u.washington.edu (Guy Kuo)...       4   \n",
       "2  From: twillis@ec.ecn.purdue.edu (Thomas E Will...       4   \n",
       "3  From: jgreen@amber (Joe Green)\\nSubject: Re: W...       1   \n",
       "4  From: jcm@head-cfa.harvard.edu (Jonathan McDow...      14   \n",
       "\n",
       "            target_names  \n",
       "0              rec.autos  \n",
       "1  comp.sys.mac.hardware  \n",
       "2  comp.sys.mac.hardware  \n",
       "3          comp.graphics  \n",
       "4              sci.space  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import Dataset\n",
    "df = pd.read_json('https://raw.githubusercontent.com/selva86/datasets/master/newsgroups.json')\n",
    "print(df.target_names.unique())\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['From: (wheres my thing) Subject: WHAT car is this!? Nntp-Posting-Host: '\n",
      " 'rac3.wam.umd.edu Organization: University of Maryland, College Park Lines: '\n",
      " '15 I was wondering if anyone out there could enlighten me on this car I saw '\n",
      " 'the other day. It was a 2-door sports car, looked to be from the late 60s/ '\n",
      " 'early 70s. It was called a Bricklin. The doors were really small. In '\n",
      " 'addition, the front bumper was separate from the rest of the body. This is '\n",
      " 'all I know. If anyone can tellme a model name, engine specs, years of '\n",
      " 'production, where this car is made, history, or whatever info you have on '\n",
      " 'this funky looking car, please e-mail. Thanks, - IL ---- brought to you by '\n",
      " 'your neighborhood Lerxst ---- ']\n"
     ]
    }
   ],
   "source": [
    "# Convert to list\n",
    "data = df.content.values.tolist()\n",
    "\n",
    "# Remove Emails\n",
    "data = [re.sub('\\S*@\\S*\\s?', '', sent) for sent in data]\n",
    "\n",
    "# Remove new line characters\n",
    "data = [re.sub('\\s+', ' ', sent) for sent in data]\n",
    "\n",
    "# Remove distracting single quotes\n",
    "data = [re.sub(\"\\'\", \"\", sent) for sent in data]\n",
    "\n",
    "pprint(data[:1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['from', 'wheres', 'my', 'thing', 'subject', 'what', 'car', 'is', 'this', 'nntp', 'posting', 'host', 'rac', 'wam', 'umd', 'edu', 'organization', 'university', 'of', 'maryland', 'college', 'park', 'lines', 'was', 'wondering', 'if', 'anyone', 'out', 'there', 'could', 'enlighten', 'me', 'on', 'this', 'car', 'saw', 'the', 'other', 'day', 'it', 'was', 'door', 'sports', 'car', 'looked', 'to', 'be', 'from', 'the', 'late', 'early', 'it', 'was', 'called', 'bricklin', 'the', 'doors', 'were', 'really', 'small', 'in', 'addition', 'the', 'front', 'bumper', 'was', 'separate', 'from', 'the', 'rest', 'of', 'the', 'body', 'this', 'is', 'all', 'know', 'if', 'anyone', 'can', 'tellme', 'model', 'name', 'engine', 'specs', 'years', 'of', 'production', 'where', 'this', 'car', 'is', 'made', 'history', 'or', 'whatever', 'info', 'you', 'have', 'on', 'this', 'funky', 'looking', 'car', 'please', 'mail', 'thanks', 'il', 'brought', 'to', 'you', 'by', 'your', 'neighborhood', 'lerxst']]\n"
     ]
    }
   ],
   "source": [
    "# simple_preprocess -> Convert a document into a list of tokens.\n",
    "# This lowercases, tokenizes, de-accents (optional). – the output are final tokens = unicode strings, \n",
    "# that won’t be processed any further.\n",
    "\n",
    "def sent_to_words(sentences):\n",
    "    for sentence in sentences:\n",
    "        yield(gensim.utils.simple_preprocess(str(sentence), deacc=True))  # deacc=True removes punctuations\n",
    "\n",
    "data_words = list(sent_to_words(data))\n",
    "\n",
    "print(data_words[:1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['from', 'wheres', 'my', 'thing', 'subject', 'what', 'car', 'is', 'this', 'nntp_posting_host', 'rac_wam_umd_edu', 'organization', 'university', 'of', 'maryland_college_park', 'lines', 'was', 'wondering', 'if', 'anyone', 'out', 'there', 'could', 'enlighten', 'me', 'on', 'this', 'car', 'saw', 'the', 'other', 'day', 'it', 'was', 'door', 'sports', 'car', 'looked', 'to', 'be', 'from', 'the', 'late', 'early', 'it', 'was', 'called', 'bricklin', 'the', 'doors', 'were', 'really', 'small', 'in', 'addition', 'the', 'front_bumper', 'was', 'separate', 'from', 'the', 'rest', 'of', 'the', 'body', 'this', 'is', 'all', 'know', 'if', 'anyone', 'can', 'tellme', 'model', 'name', 'engine', 'specs', 'years', 'of', 'production', 'where', 'this', 'car', 'is', 'made', 'history', 'or', 'whatever', 'info', 'you', 'have', 'on', 'this', 'funky', 'looking', 'car', 'please', 'mail', 'thanks', 'il', 'brought', 'to', 'you', 'by', 'your', 'neighborhood', 'lerxst']\n"
     ]
    }
   ],
   "source": [
    "# Build the bigram and trigram models\n",
    "bigram = gensim.models.Phrases(data_words, min_count=5, threshold=100) # higher threshold fewer phrases.\n",
    "trigram = gensim.models.Phrases(bigram[data_words], threshold=100)  \n",
    "\n",
    "# Faster way to get a sentence clubbed as a trigram/bigram\n",
    "bigram_mod = gensim.models.phrases.Phraser(bigram)\n",
    "trigram_mod = gensim.models.phrases.Phraser(trigram)\n",
    "\n",
    "# See trigram example\n",
    "print(trigram_mod[bigram_mod[data_words[0]]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define functions for stopwords, bigrams, trigrams and lemmatization\n",
    "def remove_stopwords(texts):\n",
    "    return [[word for word in simple_preprocess(str(doc)) if word not in stop_words] for doc in texts]\n",
    "\n",
    "def make_bigrams(texts):\n",
    "    return [bigram_mod[doc] for doc in texts]\n",
    "\n",
    "def make_trigrams(texts):\n",
    "    return [trigram_mod[bigram_mod[doc]] for doc in texts]\n",
    "\n",
    "def lemmatization(texts, allowed_postags=['NOUN', 'ADJ', 'VERB', 'ADV']):\n",
    "    \"\"\"https://spacy.io/api/annotation\"\"\"\n",
    "    texts_out = []\n",
    "    for sent in texts:\n",
    "        doc = nlp(\" \".join(sent)) \n",
    "        texts_out.append([token.lemma_ for token in doc if token.pos_ in allowed_postags])\n",
    "    return texts_out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['where', 's', 'thing', 'car', 'nntp_poste', 'host', 'umd', 'organization', 'university', 'maryland_college', 'park', 'line', 'wonder', 'anyone', 'could', 'enlighten', 'car', 'see', 'day', 'door', 'sport', 'car', 'look', 'late', 'early', 'call', 'bricklin', 'door', 'really', 'small', 'addition', 'front_bumper', 'separate', 'rest', 'body', 'know', 'anyone', 'tellme', 'model', 'name', 'engine', 'specs', 'year', 'production', 'car', 'make', 'history', 'info', 'funky', 'look', 'car', 'mail', 'thank', 'bring', 'neighborhood', 'lerxst']]\n"
     ]
    }
   ],
   "source": [
    "# Remove Stop Words\n",
    "data_words_nostops = remove_stopwords(data_words)\n",
    "\n",
    "# Form Bigrams\n",
    "data_words_bigrams = make_bigrams(data_words_nostops)\n",
    "\n",
    "# Initialize spacy 'en' model, keeping only tagger component (for efficiency)\n",
    "# python3 -m spacy download en\n",
    "nlp = spacy.load('en', disable=['parser', 'ner'])\n",
    "\n",
    "# Do lemmatization keeping only noun, adj, vb, adv\n",
    "data_lemmatized = lemmatization(data_words_bigrams, allowed_postags=['NOUN', 'ADJ', 'VERB', 'ADV'])\n",
    "\n",
    "print(data_lemmatized[:1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[(0, 1), (1, 2), (2, 1), (3, 1), (4, 1), (5, 1), (6, 5), (7, 1), (8, 1), (9, 2), (10, 1), (11, 1), (12, 1), (13, 1), (14, 1), (15, 1), (16, 1), (17, 1), (18, 1), (19, 1), (20, 1), (21, 1), (22, 2), (23, 1), (24, 1), (25, 1), (26, 1), (27, 1), (28, 1), (29, 1), (30, 1), (31, 1), (32, 1), (33, 1), (34, 1), (35, 1), (36, 1), (37, 1), (38, 1), (39, 1), (40, 1), (41, 1), (42, 1), (43, 1), (44, 1), (45, 1), (46, 1), (47, 1), (48, 1)]]\n"
     ]
    }
   ],
   "source": [
    "# Create Dictionary\n",
    "id2word = corpora.Dictionary(data_lemmatized)\n",
    "\n",
    "# Create Corpus\n",
    "texts = data_lemmatized\n",
    "\n",
    "# Term Document Frequency\n",
    "# doc2bow() simply counts the number of occurrences of each distinct word, converts the word to its\n",
    "# integer word id and returns the result as a sparse vector. \n",
    "# The sparse vector [(0, 1), (1, 1)] therefore reads: in the document “Human computer interaction”, \n",
    "# the words computer (id 0) and human (id 1) appear once; the other ten dictionary words appear \n",
    "# (implicitly) zero times.\n",
    "corpus = [id2word.doc2bow(text) for text in texts]\n",
    "\n",
    "# View\n",
    "print(corpus[:1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'addition'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "id2word[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[('addition', 1),\n",
       "  ('anyone', 2),\n",
       "  ('body', 1),\n",
       "  ('bricklin', 1),\n",
       "  ('bring', 1),\n",
       "  ('call', 1),\n",
       "  ('car', 5),\n",
       "  ('could', 1),\n",
       "  ('day', 1),\n",
       "  ('door', 2),\n",
       "  ('early', 1),\n",
       "  ('engine', 1),\n",
       "  ('enlighten', 1),\n",
       "  ('front_bumper', 1),\n",
       "  ('funky', 1),\n",
       "  ('history', 1),\n",
       "  ('host', 1),\n",
       "  ('info', 1),\n",
       "  ('know', 1),\n",
       "  ('late', 1),\n",
       "  ('lerxst', 1),\n",
       "  ('line', 1),\n",
       "  ('look', 2),\n",
       "  ('mail', 1),\n",
       "  ('make', 1),\n",
       "  ('maryland_college', 1),\n",
       "  ('model', 1),\n",
       "  ('name', 1),\n",
       "  ('neighborhood', 1),\n",
       "  ('nntp_poste', 1),\n",
       "  ('organization', 1),\n",
       "  ('park', 1),\n",
       "  ('production', 1),\n",
       "  ('really', 1),\n",
       "  ('rest', 1),\n",
       "  ('s', 1),\n",
       "  ('see', 1),\n",
       "  ('separate', 1),\n",
       "  ('small', 1),\n",
       "  ('specs', 1),\n",
       "  ('sport', 1),\n",
       "  ('tellme', 1),\n",
       "  ('thank', 1),\n",
       "  ('thing', 1),\n",
       "  ('umd', 1),\n",
       "  ('university', 1),\n",
       "  ('where', 1),\n",
       "  ('wonder', 1),\n",
       "  ('year', 1)]]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Human readable format of corpus (term-frequency)\n",
    "[[(id2word[id], freq) for id, freq in cp] for cp in corpus[:1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Build LDA model\n",
    "lda_model = gensim.models.ldamodel.LdaModel(corpus=corpus,\n",
    "                                           id2word=id2word,\n",
    "                                           num_topics=20, \n",
    "                                           random_state=100,\n",
    "                                           update_every=1,\n",
    "                                           chunksize=100,\n",
    "                                           passes=10,\n",
    "                                           alpha='auto',\n",
    "                                           per_word_topics=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0, '0.072*\"clipper\" + 0.067*\"algorithm\" + 0.052*\"clipper_chip\" + 0.051*\"rsa\" + 0.033*\"crypto\" + 0.024*\"export\" + 0.020*\"white_house\" + 0.020*\"specification\" + 0.019*\"device\" + 0.017*\"enable\"'), (1, '0.075*\"god\" + 0.040*\"evidence\" + 0.037*\"christian\" + 0.027*\"reason\" + 0.024*\"believe\" + 0.023*\"faith\" + 0.019*\"bible\" + 0.019*\"sense\" + 0.017*\"religion\" + 0.015*\"exist\"'), (2, '0.101*\"test\" + 0.068*\"server\" + 0.045*\"motif\" + 0.030*\"resource\" + 0.026*\"workstation\" + 0.021*\"ron\" + 0.019*\"roger\" + 0.019*\"jr\" + 0.017*\"last_night\" + 0.016*\"modify\"'), (3, '0.113*\"space\" + 0.031*\"earth\" + 0.028*\"launch\" + 0.026*\"moon\" + 0.025*\"mission\" + 0.024*\"orbit\" + 0.023*\"nasa\" + 0.020*\"satellite\" + 0.018*\"mar\" + 0.016*\"rocket\"'), (4, '0.052*\"armenian\" + 0.040*\"greek\" + 0.038*\"village\" + 0.032*\"turk\" + 0.030*\"soldier\" + 0.027*\"turkish\" + 0.021*\"turkey\" + 0.018*\"cub\" + 0.013*\"inhabitant\" + 0.013*\"km\"'), (5, '0.067*\"player\" + 0.045*\"season\" + 0.035*\"brain\" + 0.034*\"nhl\" + 0.033*\"scott\" + 0.032*\"chicago\" + 0.028*\"boston\" + 0.026*\"pen\" + 0.022*\"trade\" + 0.020*\"smith\"'), (6, '0.034*\"plane\" + 0.031*\"flight\" + 0.029*\"homosexual\" + 0.027*\"planet\" + 0.026*\"dual\" + 0.021*\"production\" + 0.020*\"martin\" + 0.018*\"compute\" + 0.018*\"nt\" + 0.017*\"journal\"'), (7, '0.148*\"mac\" + 0.080*\"scsi\" + 0.063*\"newsgroup\" + 0.046*\"headache\" + 0.033*\"font\" + 0.024*\"jason\" + 0.021*\"quadra\" + 0.013*\"tip\" + 0.013*\"ide_vs\" + 0.012*\"soft\"'), (8, '0.705*\"ax\" + 0.053*\"max\" + 0.022*\"mb\" + 0.013*\"material\" + 0.010*\"cable\" + 0.010*\"circuit\" + 0.008*\"intel\" + 0.006*\"connector\" + 0.004*\"film\" + 0.003*\"voltage\"'), (9, '0.084*\"belief\" + 0.071*\"science\" + 0.060*\"christianity\" + 0.049*\"revelation\" + 0.038*\"scientific\" + 0.036*\"interpretation\" + 0.023*\"energy\" + 0.023*\"observation\" + 0.019*\"interpret\" + 0.017*\"absolute\"'), (10, '0.031*\"line\" + 0.027*\"organization\" + 0.015*\"host\" + 0.014*\"university\" + 0.014*\"use\" + 0.013*\"system\" + 0.009*\"nntp_poste\" + 0.008*\"thank\" + 0.008*\"also\" + 0.008*\"need\"'), (11, '0.159*\"key\" + 0.087*\"chip\" + 0.041*\"master\" + 0.035*\"security\" + 0.030*\"family\" + 0.029*\"bit\" + 0.026*\"secure\" + 0.024*\"secret\" + 0.024*\"public\" + 0.024*\"block\"'), (12, '0.091*\"physical\" + 0.064*\"encryption\" + 0.057*\"pin\" + 0.037*\"direct\" + 0.036*\"period\" + 0.032*\"pgp\" + 0.030*\"wife\" + 0.029*\"processor\" + 0.018*\"privacy\" + 0.017*\"adam\"'), (13, '0.042*\"not\" + 0.026*\"do\" + 0.023*\"write\" + 0.023*\"would\" + 0.020*\"be\" + 0.018*\"article\" + 0.016*\"know\" + 0.016*\"get\" + 0.015*\"think\" + 0.015*\"go\"'), (14, '0.020*\"car\" + 0.014*\"high\" + 0.014*\"power\" + 0.014*\"buy\" + 0.013*\"new\" + 0.013*\"price\" + 0.012*\"sell\" + 0.011*\"cost\" + 0.011*\"drive\" + 0.011*\"low\"'), (15, '0.045*\"die\" + 0.043*\"everything\" + 0.031*\"wire\" + 0.022*\"fire\" + 0.022*\"eat\" + 0.019*\"son\" + 0.019*\"signal\" + 0.019*\"father\" + 0.016*\"convert\" + 0.015*\"fbi\"'), (16, '0.021*\"people\" + 0.017*\"state\" + 0.013*\"government\" + 0.012*\"law\" + 0.011*\"israel\" + 0.011*\"gun\" + 0.011*\"right\" + 0.010*\"child\" + 0.009*\"issue\" + 0.008*\"kill\"'), (17, '0.073*\"window\" + 0.071*\"drive\" + 0.054*\"card\" + 0.038*\"driver\" + 0.028*\"memory\" + 0.024*\"screen\" + 0.024*\"pc\" + 0.024*\"video\" + 0.024*\"disk\" + 0.023*\"mode\"'), (18, '0.090*\"team\" + 0.089*\"game\" + 0.062*\"win\" + 0.051*\"play\" + 0.049*\"year\" + 0.022*\"score\" + 0.021*\"wing\" + 0.020*\"goal\" + 0.019*\"run\" + 0.016*\"division\"'), (19, '0.100*\"patient\" + 0.074*\"drug\" + 0.052*\"disease\" + 0.050*\"health\" + 0.043*\"treatment\" + 0.035*\"medical\" + 0.018*\"medicine\" + 0.018*\"pittsburgh\" + 0.016*\"preach\" + 0.015*\"physician\"')]\n"
     ]
    }
   ],
   "source": [
    "print(lda_model.print_topics())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'show_topics' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-22-51213a061fa4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mshow_topics\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_topics\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_words\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlog\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mformatted\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'show_topics' is not defined"
     ]
    }
   ],
   "source": [
    "show_topics(num_topics=10, num_words=10, log=False, formatted=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.072*\"clipper\" + 0.067*\"algorithm\" + 0.052*\"clipper_chip\" + 0.051*\"rsa\" + 0.033*\"crypto\" + 0.024*\"export\" + 0.020*\"white_house\" + 0.020*\"specification\" + 0.019*\"device\" + 0.017*\"enable\"\n",
      "0.075*\"god\" + 0.040*\"evidence\" + 0.037*\"christian\" + 0.027*\"reason\" + 0.024*\"believe\" + 0.023*\"faith\" + 0.019*\"bible\" + 0.019*\"sense\" + 0.017*\"religion\" + 0.015*\"exist\"\n",
      "0.101*\"test\" + 0.068*\"server\" + 0.045*\"motif\" + 0.030*\"resource\" + 0.026*\"workstation\" + 0.021*\"ron\" + 0.019*\"roger\" + 0.019*\"jr\" + 0.017*\"last_night\" + 0.016*\"modify\"\n",
      "0.113*\"space\" + 0.031*\"earth\" + 0.028*\"launch\" + 0.026*\"moon\" + 0.025*\"mission\" + 0.024*\"orbit\" + 0.023*\"nasa\" + 0.020*\"satellite\" + 0.018*\"mar\" + 0.016*\"rocket\"\n",
      "0.052*\"armenian\" + 0.040*\"greek\" + 0.038*\"village\" + 0.032*\"turk\" + 0.030*\"soldier\" + 0.027*\"turkish\" + 0.021*\"turkey\" + 0.018*\"cub\" + 0.013*\"inhabitant\" + 0.013*\"km\"\n",
      "0.067*\"player\" + 0.045*\"season\" + 0.035*\"brain\" + 0.034*\"nhl\" + 0.033*\"scott\" + 0.032*\"chicago\" + 0.028*\"boston\" + 0.026*\"pen\" + 0.022*\"trade\" + 0.020*\"smith\"\n",
      "0.034*\"plane\" + 0.031*\"flight\" + 0.029*\"homosexual\" + 0.027*\"planet\" + 0.026*\"dual\" + 0.021*\"production\" + 0.020*\"martin\" + 0.018*\"compute\" + 0.018*\"nt\" + 0.017*\"journal\"\n",
      "0.148*\"mac\" + 0.080*\"scsi\" + 0.063*\"newsgroup\" + 0.046*\"headache\" + 0.033*\"font\" + 0.024*\"jason\" + 0.021*\"quadra\" + 0.013*\"tip\" + 0.013*\"ide_vs\" + 0.012*\"soft\"\n",
      "0.705*\"ax\" + 0.053*\"max\" + 0.022*\"mb\" + 0.013*\"material\" + 0.010*\"cable\" + 0.010*\"circuit\" + 0.008*\"intel\" + 0.006*\"connector\" + 0.004*\"film\" + 0.003*\"voltage\"\n",
      "0.084*\"belief\" + 0.071*\"science\" + 0.060*\"christianity\" + 0.049*\"revelation\" + 0.038*\"scientific\" + 0.036*\"interpretation\" + 0.023*\"energy\" + 0.023*\"observation\" + 0.019*\"interpret\" + 0.017*\"absolute\"\n",
      "0.031*\"line\" + 0.027*\"organization\" + 0.015*\"host\" + 0.014*\"university\" + 0.014*\"use\" + 0.013*\"system\" + 0.009*\"nntp_poste\" + 0.008*\"thank\" + 0.008*\"also\" + 0.008*\"need\"\n",
      "0.159*\"key\" + 0.087*\"chip\" + 0.041*\"master\" + 0.035*\"security\" + 0.030*\"family\" + 0.029*\"bit\" + 0.026*\"secure\" + 0.024*\"secret\" + 0.024*\"public\" + 0.024*\"block\"\n",
      "0.091*\"physical\" + 0.064*\"encryption\" + 0.057*\"pin\" + 0.037*\"direct\" + 0.036*\"period\" + 0.032*\"pgp\" + 0.030*\"wife\" + 0.029*\"processor\" + 0.018*\"privacy\" + 0.017*\"adam\"\n",
      "0.042*\"not\" + 0.026*\"do\" + 0.023*\"write\" + 0.023*\"would\" + 0.020*\"be\" + 0.018*\"article\" + 0.016*\"know\" + 0.016*\"get\" + 0.015*\"think\" + 0.015*\"go\"\n",
      "0.020*\"car\" + 0.014*\"high\" + 0.014*\"power\" + 0.014*\"buy\" + 0.013*\"new\" + 0.013*\"price\" + 0.012*\"sell\" + 0.011*\"cost\" + 0.011*\"drive\" + 0.011*\"low\"\n",
      "0.045*\"die\" + 0.043*\"everything\" + 0.031*\"wire\" + 0.022*\"fire\" + 0.022*\"eat\" + 0.019*\"son\" + 0.019*\"signal\" + 0.019*\"father\" + 0.016*\"convert\" + 0.015*\"fbi\"\n",
      "0.021*\"people\" + 0.017*\"state\" + 0.013*\"government\" + 0.012*\"law\" + 0.011*\"israel\" + 0.011*\"gun\" + 0.011*\"right\" + 0.010*\"child\" + 0.009*\"issue\" + 0.008*\"kill\"\n",
      "0.073*\"window\" + 0.071*\"drive\" + 0.054*\"card\" + 0.038*\"driver\" + 0.028*\"memory\" + 0.024*\"screen\" + 0.024*\"pc\" + 0.024*\"video\" + 0.024*\"disk\" + 0.023*\"mode\"\n",
      "0.090*\"team\" + 0.089*\"game\" + 0.062*\"win\" + 0.051*\"play\" + 0.049*\"year\" + 0.022*\"score\" + 0.021*\"wing\" + 0.020*\"goal\" + 0.019*\"run\" + 0.016*\"division\"\n"
     ]
    }
   ],
   "source": [
    "for i in range(0, lda_model.num_topics-1):\n",
    "    print(lda_model.print_topic(i))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0,\n",
      "  '0.072*\"clipper\" + 0.067*\"algorithm\" + 0.052*\"clipper_chip\" + 0.051*\"rsa\" + '\n",
      "  '0.033*\"crypto\" + 0.024*\"export\" + 0.020*\"white_house\" + '\n",
      "  '0.020*\"specification\" + 0.019*\"device\" + 0.017*\"enable\"'),\n",
      " (1,\n",
      "  '0.075*\"god\" + 0.040*\"evidence\" + 0.037*\"christian\" + 0.027*\"reason\" + '\n",
      "  '0.024*\"believe\" + 0.023*\"faith\" + 0.019*\"bible\" + 0.019*\"sense\" + '\n",
      "  '0.017*\"religion\" + 0.015*\"exist\"'),\n",
      " (2,\n",
      "  '0.101*\"test\" + 0.068*\"server\" + 0.045*\"motif\" + 0.030*\"resource\" + '\n",
      "  '0.026*\"workstation\" + 0.021*\"ron\" + 0.019*\"roger\" + 0.019*\"jr\" + '\n",
      "  '0.017*\"last_night\" + 0.016*\"modify\"'),\n",
      " (3,\n",
      "  '0.113*\"space\" + 0.031*\"earth\" + 0.028*\"launch\" + 0.026*\"moon\" + '\n",
      "  '0.025*\"mission\" + 0.024*\"orbit\" + 0.023*\"nasa\" + 0.020*\"satellite\" + '\n",
      "  '0.018*\"mar\" + 0.016*\"rocket\"'),\n",
      " (4,\n",
      "  '0.052*\"armenian\" + 0.040*\"greek\" + 0.038*\"village\" + 0.032*\"turk\" + '\n",
      "  '0.030*\"soldier\" + 0.027*\"turkish\" + 0.021*\"turkey\" + 0.018*\"cub\" + '\n",
      "  '0.013*\"inhabitant\" + 0.013*\"km\"'),\n",
      " (5,\n",
      "  '0.067*\"player\" + 0.045*\"season\" + 0.035*\"brain\" + 0.034*\"nhl\" + '\n",
      "  '0.033*\"scott\" + 0.032*\"chicago\" + 0.028*\"boston\" + 0.026*\"pen\" + '\n",
      "  '0.022*\"trade\" + 0.020*\"smith\"'),\n",
      " (6,\n",
      "  '0.034*\"plane\" + 0.031*\"flight\" + 0.029*\"homosexual\" + 0.027*\"planet\" + '\n",
      "  '0.026*\"dual\" + 0.021*\"production\" + 0.020*\"martin\" + 0.018*\"compute\" + '\n",
      "  '0.018*\"nt\" + 0.017*\"journal\"'),\n",
      " (7,\n",
      "  '0.148*\"mac\" + 0.080*\"scsi\" + 0.063*\"newsgroup\" + 0.046*\"headache\" + '\n",
      "  '0.033*\"font\" + 0.024*\"jason\" + 0.021*\"quadra\" + 0.013*\"tip\" + '\n",
      "  '0.013*\"ide_vs\" + 0.012*\"soft\"'),\n",
      " (8,\n",
      "  '0.705*\"ax\" + 0.053*\"max\" + 0.022*\"mb\" + 0.013*\"material\" + 0.010*\"cable\" + '\n",
      "  '0.010*\"circuit\" + 0.008*\"intel\" + 0.006*\"connector\" + 0.004*\"film\" + '\n",
      "  '0.003*\"voltage\"'),\n",
      " (9,\n",
      "  '0.084*\"belief\" + 0.071*\"science\" + 0.060*\"christianity\" + '\n",
      "  '0.049*\"revelation\" + 0.038*\"scientific\" + 0.036*\"interpretation\" + '\n",
      "  '0.023*\"energy\" + 0.023*\"observation\" + 0.019*\"interpret\" + '\n",
      "  '0.017*\"absolute\"'),\n",
      " (10,\n",
      "  '0.031*\"line\" + 0.027*\"organization\" + 0.015*\"host\" + 0.014*\"university\" + '\n",
      "  '0.014*\"use\" + 0.013*\"system\" + 0.009*\"nntp_poste\" + 0.008*\"thank\" + '\n",
      "  '0.008*\"also\" + 0.008*\"need\"'),\n",
      " (11,\n",
      "  '0.159*\"key\" + 0.087*\"chip\" + 0.041*\"master\" + 0.035*\"security\" + '\n",
      "  '0.030*\"family\" + 0.029*\"bit\" + 0.026*\"secure\" + 0.024*\"secret\" + '\n",
      "  '0.024*\"public\" + 0.024*\"block\"'),\n",
      " (12,\n",
      "  '0.091*\"physical\" + 0.064*\"encryption\" + 0.057*\"pin\" + 0.037*\"direct\" + '\n",
      "  '0.036*\"period\" + 0.032*\"pgp\" + 0.030*\"wife\" + 0.029*\"processor\" + '\n",
      "  '0.018*\"privacy\" + 0.017*\"adam\"'),\n",
      " (13,\n",
      "  '0.042*\"not\" + 0.026*\"do\" + 0.023*\"write\" + 0.023*\"would\" + 0.020*\"be\" + '\n",
      "  '0.018*\"article\" + 0.016*\"know\" + 0.016*\"get\" + 0.015*\"think\" + 0.015*\"go\"'),\n",
      " (14,\n",
      "  '0.020*\"car\" + 0.014*\"high\" + 0.014*\"power\" + 0.014*\"buy\" + 0.013*\"new\" + '\n",
      "  '0.013*\"price\" + 0.012*\"sell\" + 0.011*\"cost\" + 0.011*\"drive\" + 0.011*\"low\"'),\n",
      " (15,\n",
      "  '0.045*\"die\" + 0.043*\"everything\" + 0.031*\"wire\" + 0.022*\"fire\" + '\n",
      "  '0.022*\"eat\" + 0.019*\"son\" + 0.019*\"signal\" + 0.019*\"father\" + '\n",
      "  '0.016*\"convert\" + 0.015*\"fbi\"'),\n",
      " (16,\n",
      "  '0.021*\"people\" + 0.017*\"state\" + 0.013*\"government\" + 0.012*\"law\" + '\n",
      "  '0.011*\"israel\" + 0.011*\"gun\" + 0.011*\"right\" + 0.010*\"child\" + '\n",
      "  '0.009*\"issue\" + 0.008*\"kill\"'),\n",
      " (17,\n",
      "  '0.073*\"window\" + 0.071*\"drive\" + 0.054*\"card\" + 0.038*\"driver\" + '\n",
      "  '0.028*\"memory\" + 0.024*\"screen\" + 0.024*\"pc\" + 0.024*\"video\" + 0.024*\"disk\" '\n",
      "  '+ 0.023*\"mode\"'),\n",
      " (18,\n",
      "  '0.090*\"team\" + 0.089*\"game\" + 0.062*\"win\" + 0.051*\"play\" + 0.049*\"year\" + '\n",
      "  '0.022*\"score\" + 0.021*\"wing\" + 0.020*\"goal\" + 0.019*\"run\" + '\n",
      "  '0.016*\"division\"'),\n",
      " (19,\n",
      "  '0.100*\"patient\" + 0.074*\"drug\" + 0.052*\"disease\" + 0.050*\"health\" + '\n",
      "  '0.043*\"treatment\" + 0.035*\"medical\" + 0.018*\"medicine\" + 0.018*\"pittsburgh\" '\n",
      "  '+ 0.016*\"preach\" + 0.015*\"physician\"')]\n"
     ]
    }
   ],
   "source": [
    "# Print the Keyword in the 10 topics\n",
    "pprint(lda_model.print_topics())\n",
    "doc_lda = lda_model[corpus]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Perplexity:  -14.176559763680439\n",
      "\n",
      "Coherence Score:  0.5163890312318851\n"
     ]
    }
   ],
   "source": [
    "# Compute Perplexity\n",
    "print('\\nPerplexity: ', lda_model.log_perplexity(corpus))  # a measure of how good the model is. lower the better.\n",
    "\n",
    "# Compute Coherence Score\n",
    "coherence_model_lda = CoherenceModel(model=lda_model, texts=data_lemmatized, dictionary=id2word, coherence='c_v')\n",
    "coherence_lda = coherence_model_lda.get_coherence()\n",
    "print('\\nCoherence Score: ', coherence_lda)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/deepshikha/.local/lib/python3.6/site-packages/pyLDAvis/_prepare.py:257: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=False'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
      "\n",
      "  return pd.concat([default_term_info] + list(topic_dfs))\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.css\">\n",
       "\n",
       "\n",
       "<div id=\"ldavis_el263641405607683543684884629838\"></div>\n",
       "<script type=\"text/javascript\">\n",
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\"christian\", \"christianity\", \"church\", \"circuit\", \"citizen\", \"claim\", \"claim\", \"claim\", \"clh\", \"clipper\", \"clipper_chip\", \"close\", \"close\", \"close\", \"close\", \"close\", \"cmu\", \"code\", \"cole\", \"color\", \"com\", \"come\", \"come\", \"compress\", \"compression\", \"compute\", \"computer\", \"computer\", \"computer_science\", \"computer_science\", \"conclusion\", \"conclusion\", \"conclusion\", \"connector\", \"console\", \"constitution\", \"contrib\", \"controversial\", \"convert\", \"conviction\", \"cool\", \"copy\", \"cord\", \"core\", \"core\", \"corp\", \"cost\", \"could\", \"could\", \"could\", \"country\", \"court\", \"cover\", \"cover\", \"cpu\", \"cr\", \"creator\", \"crime\", \"criminal\", \"crypto\", \"cryptography\", \"cub\", \"curve\", \"custom\", \"dale\", \"datum\", \"datum\", \"datum\", \"daughter\", \"dave\", \"dave\", \"dc\", \"dealer\", \"dean\", \"death\", \"death\", \"default\", \"defense\", \"defense\", \"defensive\", \"derive\", \"designer\", \"detroit\", \"develop\", \"develop\", \"develop\", \"develop\", \"device\", \"device\", \"device\", \"diameter\", \"die\", \"digital_equipment\", \"direct\", \"dirty\", \"disease\", \"disk\", \"distinguish\", \"division\", \"division\", \"division\", \"do\", \"do\", \"doctor\", \"dod\", \"download\", \"dr\", \"draft\", \"draft\", \"dream\", \"drive\", \"drive\", \"driver\", \"driver\", \"drug\", \"dual\", \"earth\", \"earth\", \"earth\", \"eat\", \"edition\", \"electrical\", \"email\", \"enable\", \"encryption\", \"end\", \"end\", \"end\", \"end\", \"energy\", \"engine\", \"engineering_carnegie\", \"enhanced_mode\", \"entity\", \"entry\", \"environmental\", \"era\", \"escape\", \"escape\", \"esdi\", \"ethnic\", \"european\", \"european\", \"european\", \"european\", \"european\", \"eve\", \"even\", \"even\", \"ever\", \"everything\", \"evidence\", \"evidence\", \"excellent_condition\", \"exchange\", \"exchange\", \"exist\", \"exist\", \"exist\", \"experiment\", \"expire\", \"explain\", \"explain\", \"explain\", \"export\", \"exposure\", \"express_access\", \"extermination_ohanus\", \"face\", \"fact\", \"fact\", \"fact\", \"factory\", \"faith\", \"false\", \"false\", \"false\", \"fame\", \"family\", \"far\", \"far\", \"fasst\", \"fast\", \"fast\", \"fast\", \"father\", \"fbi\", \"feel\", \"fight\", \"file\", \"film\", \"final\", \"final\", \"final\", \"final\", \"final\", \"fire\", \"fire\", \"fire\", \"first\", \"first\", \"first\", \"first\", \"first\", \"first\", \"flexible\", \"flight\", \"floor\", \"floppy\", \"flyer\", \"font\", \"force\", \"ford\", \"found\", \"found\", \"frame\", \"freedom\", \"french\", \"front\", \"galileo\", \"game\", \"garage\", \"gary\", \"gateway\", \"gen\", \"genesis\", \"genocide\", \"german\", \"get\", \"get\", \"get\", \"giant\", \"go\", \"go\", \"goal\", \"goal\", \"god\", \"good\", \"good\", \"good\", \"good\", \"government\", \"government\", \"gravity\", \"greatly_appreciate\", \"greek\", \"greek\", \"ground\", \"ground\", \"group\", \"group\", \"group\", \"grubb\", \"guess\", \"guilty\", \"gun\", \"hand\", \"happen\", \"happily\", \"hardware\", \"hardware\", \"harris\", \"hassle\", \"have\", \"have\", \"head\", \"head\", \"headache\", \"health\", \"hear\", \"hell\", \"help\", \"help\", \"high\", \"high\", \"high\", \"high\", \"hit\", \"hit\", \"hit\", \"hitter\", \"homeland\", \"homosexual\", \"host\", \"hot\", \"hull\", \"human\", \"human\", \"human\", \"human_being\", \"ibm\", \"ide_vs\", \"ii\", \"image_processe\", \"imprison\", \"inc\", \"include\", \"include\", \"increase\", \"increase\", \"info\", \"information\", \"inhabitant\", \"inning\", \"input\", \"insert\", \"insurance\", \"intel\", \"intentionally\", \"interested\", \"internet\", \"interpret\", \"interpretation\", \"iran\", \"isa\", \"islam\", \"islamic\", \"israel\", \"israeli\", \"issue\", \"issue\", \"issue\", \"jason\", \"jeffrey\", \"joint\", \"jone\", \"journal\", \"jpl\", \"jr\", \"keith\", \"keith_ryan\", \"kevin\", \"key\", \"keyword\", \"kill\", \"kill\", \"kill\", \"kind\", \"kinda\", \"km\", \"know\", \"know\", \"kurd\", \"lance\", \"large\", \"large\", \"large\", \"large\", \"last\", \"last\", \"last\", \"last\", \"last\", \"last_night\", \"launch\", \"law\", \"law\", \"leafs\", \"legal\", \"lemieux\", \"less\", \"less\", \"less\", \"less\", \"let\", \"let\", \"levy\", \"life\", \"life\", \"life\", \"light\", \"light\", \"line\", \"line\", \"list\", \"listen\", \"log\", \"long_term\", \"longer_exist\", \"lord\", \"lose\", \"lose\", \"louis\", \"love\", \"love\", \"low\", \"lt\", \"luck\", \"lunar\", \"lx\", \"mac\", \"machine\", \"machine\", \"mail\", \"make\", \"make\", \"make\", \"man\", \"man\", \"man\", \"mantis_consultant\", \"many\", \"many\", \"many\", \"many\", \"mar\", \"market\", \"martin\", \"massacre\", \"master\", \"material\", \"mathew\", \"max\", \"may\", \"may\", \"may\", \"may\", \"maybe\", \"mb\", \"medical\", \"medicine\", \"meg\", \"melkonian\", \"mellon_pittsburgh\", \"member\", \"memory\", \"message\", \"met\", \"mhz\", \"michigan\", \"might_serve\", \"mile\", \"milwaukee\", \"min\", \"minnesota\", \"mission\", \"misuse\", \"mode\", \"model\", \"model\", \"modify\", \"module\", \"monday\", \"monitor\", \"month\", \"month\", \"month\", \"montreal\", \"moon\", \"moral\", \"moslem\", \"mother\", \"mother\", \"mother\", \"motherboard\", \"motif\", \"motto\", \"mount\", \"mountain_passe\", \"mountain_view\", \"mouse\", \"much\", \"much\", \"much\", \"muslim\", \"muslim\", \"narrow\", \"nasa\", \"nec\", \"need\", \"need\", \"neighborhood\", \"never\", \"new\", \"new\", \"new\", \"new\", \"newsgroup\", \"newsreader_rusnews\", \"nhl\", \"nicely\", \"nist\", \"nntp_poste\", \"nntp_posting\", \"north_carolina\", \"norway\", \"not\", \"notice\", \"nsa\", \"nt\", \"number\", \"number\", \"number\", \"observation\", \"observatory\", \"observe\", \"observe\", \"observe\", \"occupation\", \"oct\", \"octopus\", \"offense\", \"old\", \"old\", \"old\", \"ontario\", \"opposite\", \"orbit\", \"orbital\", \"organization\", \"organization\", \"original_sender\", \"out\", \"owner\", \"ozone\", \"painless\", \"partially\", \"partly\", \"passage\", \"patient\", \"pay\", \"pay\", \"pay\", \"pc\", \"pen\", \"penguin\", \"pennsylvania\", \"people\", \"people\", \"people\", \"people\", \"percent\", \"period\", \"person\", \"person\", \"person\", \"peter\", \"pgp\", \"phone\", \"phone\", \"physical\", \"physician\", \"physicist\", \"pick\", \"pick\", \"pick\", \"pin\", \"pitch\", \"pitt\", \"pittsburgh\", \"pittsburgh\", \"plane\", \"planet\", \"planetary\", \"play\", \"play\", \"player\", \"please_respond\", \"point\", \"point\", \"point\", \"point\", \"point\", \"pole\", \"police\", \"policy\", \"port\", \"post\", \"post\", \"power\", \"power\", \"power_supply\", \"preach\", \"predict\", \"preferably\", \"preliminary\", \"president\", \"prevention\", \"price\", \"privacy\", \"problem\", \"problem\", \"problem\", \"proceed\", \"proceed\", \"processor\", \"production\", \"program\", \"program\", \"project\", \"project\", \"proof\", \"proposal\", \"proposal\", \"propulsion\", \"protect\", \"public\", \"public\", \"publish\", \"purpose\", \"purpose\", \"quadra\", \"qualified\", \"quick\", \"quite\", \"quran\", \"radiation\", \"radio\", \"radio_shack\", \"ram\", \"rate\", \"rational\", \"reality\", \"reality\", \"really\", \"reason\", \"reason\", \"reason\", \"rebel\", \"receiver\", \"recommend\", \"recommend\", \"redesign\", \"register\", \"register\", \"regular_season\", \"reject\", \"reject\", \"related\", \"religion\", \"religious\", \"religious\", \"remainder\", \"remember\", \"reply\", \"reply\", \"reply\", \"reply\", \"report\", \"report\", \"report\", \"report\", \"republic\", \"research\", \"research\", \"reserve_university\", \"resource\", \"revelation\", \"review\", \"richard\", \"right\", \"right\", \"risk\", \"risk\", \"risk\", \"road\", \"road\", \"rob\", \"rocket\", \"roger\", \"roman\", \"roman\", \"ron\", \"rsa\", \"run\", \"run\", \"run\", \"run\", \"run\", \"russia\", \"russian\", \"russian\", \"s\", \"sabbath\", \"sale\", \"santa_clara\", \"satan\", \"satellite\", \"say\", \"say\", \"say\", \"science\", \"science\", \"science\", \"scientific\", \"scientific\", \"score\", \"scott\", \"screen\", \"scripture\", \"scsi\", \"scsi_controler\", \"scsi_controller\", \"season\", \"second\", \"second\", \"second\", \"second\", \"secret\", \"secular\", \"secure\", \"security\", \"security\", \"see\", \"see\", \"seem\", \"sell\", \"send\", \"sense\", \"sense\", \"sense\", \"serdar_argic\", \"serial\", \"series\", \"serious\", \"serve\", \"serve\", \"server\", \"set\", \"set\", \"sgi\", \"shareware\", \"shuttle\", \"si\", \"signal\", \"silence\", \"silicon_graphic\", \"single_turkish\", \"sister\", \"sky\", \"slaughter\", \"smith\", \"society\", \"soft\", \"software\", \"solar\", \"soldier\", \"soldier\", \"someone\", \"someone\", \"something\", \"son\", \"soul_sahak\", \"source\", \"source\", \"source\", \"soviet\", \"soviet_armenia\", \"space\", \"space_digest\", \"space_shuttle\", \"space_station\", \"speak\", \"speak\", \"specification\", \"specify\", \"specify\", \"specs\", \"speed\", \"speed\", \"stall\", \"star\", \"star\", \"starter\", \"stat\", \"state\", \"state\", \"state\", \"state\", \"step\", \"stephen\", \"stick\", \"still\", \"still\", \"still\", \"study\", \"study\", \"study\", \"study\", \"study\", \"substance\", \"sue\", \"sufficiently\", \"summarize\", \"surprisingly\", \"swap\", \"switch\", \"switch\", \"synchronous\", \"system\", \"system\", \"take\", \"take\", \"talk\", \"tank\", \"tar\", \"task\", \"teaching\", \"team\", \"technology\", \"technology\", \"technology\", \"technology_pasadena\", \"telescope\", \"tell\", \"test\", \"thank\", \"theodore_kaldis\", \"there\", \"thermal\", \"thick\", \"thing\", \"think\", \"thinking\", \"though\", \"though\", \"tie\", \"tie\", \"tim\", \"time\", \"time\", \"time\", \"time\", \"timing\", \"tip\", \"toronto\", \"tour\", \"town\", \"town\", \"toyota\", \"trace\", \"trade\", \"trade\", \"trade\", \"tradition\", \"treat\", \"treat\", \"treat\", \"treatment\", \"trial\", \"troop\", \"true\", \"true\", \"true\", \"true\", \"truth\", \"turk\", \"turkey\", \"turkish\", \"type\", \"united_state\", \"united_states\", \"univ\", \"univ\", \"univ\", \"university\", \"university\", \"university\", \"upcoming\", \"upgrade\", \"use\", \"use\", \"use\", \"use\", \"use\", \"usenet\", \"utilize\", \"van\", \"variation\", \"version\", \"video\", \"view\", \"view\", \"view\", \"view\", \"village\", \"virginia\", \"voice\", \"voice\", \"vol\", \"vol\", \"voltage\", \"want\", \"want\", \"war\", \"war\", \"warrant\", \"warrant\", \"way\", \"way\", \"way\", \"way\", \"weapon\", \"weeks_ago\", \"well\", \"well\", \"well\", \"well\", \"white_house\", \"whole\", \"whole\", \"whole\", \"widget\", \"wife\", \"win\", \"window\", \"wing\", \"wire\", \"wiretap\", \"word\", \"word\", \"word\", \"word\", \"work\", \"work\", \"work\", \"work\", \"workstation\", \"would\", \"would\", \"would\", \"wound\", \"write\", \"write\", \"writing\", \"wrong\", \"wwii\", \"yankee\", \"year\", \"year\", \"year\", \"year\", \"year\"]}, \"R\": 30, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [14, 11, 17, 15, 9, 2, 18, 19, 16, 4, 5, 6, 12, 13, 3, 7, 10, 8, 20, 1]};\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_el263641405607683543684884629838\", ldavis_el263641405607683543684884629838_data);\n",
       "   }(LDAvis);\n",
       "}else if(typeof define === \"function\" && define.amd){\n",
       "   // require.js is available: use it to load d3/LDAvis\n",
       "   require.config({paths: {d3: \"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min\"}});\n",
       "   require([\"d3\"], function(d3){\n",
       "      window.d3 = d3;\n",
       "      LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
       "        new LDAvis(\"#\" + \"ldavis_el263641405607683543684884629838\", ldavis_el263641405607683543684884629838_data);\n",
       "      });\n",
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       "    // require.js not available: dynamically load d3 & LDAvis\n",
       "    LDAvis_load_lib(\"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min.js\", function(){\n",
       "         LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
       "                 new LDAvis(\"#\" + \"ldavis_el263641405607683543684884629838\", ldavis_el263641405607683543684884629838_data);\n",
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       "}\n",
       "</script>"
      ],
      "text/plain": [
       "PreparedData(topic_coordinates=              x         y  topics  cluster       Freq\n",
       "topic                                                \n",
       "13    -0.379783  0.008571       1        1  33.826191\n",
       "10    -0.351080 -0.036524       2        1  28.056835\n",
       "16    -0.318754  0.160001       3        1  10.857606\n",
       "14    -0.209900 -0.292712       4        1   7.540839\n",
       "8      0.022399 -0.042695       5        1   4.648159\n",
       "1     -0.223054  0.246557       6        1   3.075295\n",
       "17    -0.009532 -0.210048       7        1   2.136385\n",
       "18    -0.005169 -0.098735       8        1   1.656911\n",
       "15     0.074401 -0.000666       9        1   1.410655\n",
       "3      0.072080  0.017076      10        1   1.020934\n",
       "4      0.073950  0.058024      11        1   1.003426\n",
       "5      0.105719  0.011050      12        1   0.951477\n",
       "11     0.087814  0.009344      13        1   0.869672\n",
       "12     0.134442  0.018817      14        1   0.580911\n",
       "2      0.148622  0.024765      15        1   0.578891\n",
       "6      0.158269  0.025703      16        1   0.406067\n",
       "9      0.149145  0.026365      17        1   0.388500\n",
       "7      0.155069  0.025191      18        1   0.385798\n",
       "19     0.153322  0.026880      19        1   0.319311\n",
       "0      0.162041  0.023035      20        1   0.286143, topic_info=      Category          Freq     Term         Total  loglift  logprob\n",
       "5533   Default  51911.000000       ax  51911.000000  30.0000  30.0000\n",
       "80     Default  22376.000000      not  22376.000000  29.0000  29.0000\n",
       "63     Default  14055.000000       do  14055.000000  28.0000  28.0000\n",
       "48     Default   5094.000000     year   5094.000000  27.0000  27.0000\n",
       "21     Default  19601.000000     line  19601.000000  26.0000  26.0000\n",
       "...        ...           ...      ...           ...      ...      ...\n",
       "14098  Topic20      0.035222      pkp      1.242019   2.2936 -11.7656\n",
       "1549   Topic20      6.591735    voice    522.455444   1.4837  -6.5337\n",
       "4409   Topic20      1.785752  approve    206.332016   1.1068  -7.8397\n",
       "786    Topic20      3.624154  develop    576.583130   0.7869  -7.1319\n",
       "7995   Topic20      0.035223   jumper      1.242149   2.2935 -11.7656\n",
       "\n",
       "[832 rows x 6 columns], token_table=      Topic      Freq            Term\n",
       "term                                 \n",
       "1051     18  0.980103              ab\n",
       "590      17  0.988142        absolute\n",
       "3220     17  0.923188  absolute_truth\n",
       "415      18  0.950375          absurd\n",
       "1630      2  0.271452          accept\n",
       "...     ...       ...             ...\n",
       "48        1  0.337015            year\n",
       "48        3  0.165269            year\n",
       "48        4  0.240052            year\n",
       "48        8  0.251044            year\n",
       "48       10  0.006477            year\n",
       "\n",
       "[1074 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[14, 11, 17, 15, 9, 2, 18, 19, 16, 4, 5, 6, 12, 13, 3, 7, 10, 8, 20, 1])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pyLDAvis.enable_notebook()\n",
    "vis = pyLDAvis.gensim.prepare(lda_model, corpus, id2word)\n",
    "vis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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