Chapter10.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chapter 10: LDA for Topic Modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Newsgroups data\n",
    "\n",
    "As before, let's consider a specific set of categories:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import fetch_20newsgroups\n",
    "\n",
    "def load_dataset(sset, cats):\n",
    "    if cats==[]:\n",
    "        newsgroups_dset = fetch_20newsgroups(subset=sset,\n",
    "                          remove=('headers', 'footers', 'quotes'),\n",
    "                          shuffle=True)\n",
    "    else:\n",
    "        newsgroups_dset = fetch_20newsgroups(subset=sset, categories=cats,\n",
    "                          remove=('headers', 'footers', 'quotes'),\n",
    "                          shuffle=True)\n",
    "    return newsgroups_dset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9850\n"
     ]
    }
   ],
   "source": [
    "categories = [\"comp.windows.x\", \"misc.forsale\", \"rec.autos\", \"rec.motorcycles\", \"rec.sport.baseball\"]\n",
    "categories += [\"rec.sport.hockey\", \"sci.crypt\", \"sci.med\", \"sci.space\", \"talk.politics.mideast\"]\n",
    "\n",
    "newsgroups_all = load_dataset('all', categories)\n",
    "print(len(newsgroups_all.data))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocess\n",
    "\n",
    "Convert word forms to stems to get concise representations for the documents: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import nltk\n",
    "from nltk.stem import SnowballStemmer\n",
    "\n",
    "stemmer = SnowballStemmer(\"english\")\n",
    "\n",
    "def stem(text):\n",
    "    return stemmer.stem(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gensim\n",
    "from gensim.utils import simple_preprocess\n",
    "from gensim.parsing.preprocessing import STOPWORDS as stopwords\n",
    "\n",
    "#print(stopwords)\n",
    "\n",
    "def preprocess(text):\n",
    "    result = []\n",
    "    for token in gensim.utils.simple_preprocess(text, min_len=4):\n",
    "        if token not in stopwords: #and len(token) > 3:\n",
    "            result.append(stem(token))\n",
    "    return result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check how each document is represented. For example, look into the very first one:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original document: \n",
      "Hi Xperts!\n",
      "\n",
      "How can I move the cursor with the keyboard (i.e. cursor keys), \n",
      "if no mouse is available?\n",
      "\n",
      "Any hints welcome.\n",
      "\n",
      "Thanks.\n",
      "\n",
      "\n",
      "Tokenized document: \n",
      "['Hi', 'Xperts', 'How', 'can', 'I', 'move', 'the', 'cursor', 'with', 'the', 'keyboard', 'i', 'e', 'cursor', 'keys', 'if', 'no', 'mouse', 'is', 'available', 'Any', 'hints', 'welcome', 'Thanks']\n",
      "\n",
      "\n",
      "Preprocessed document: \n",
      "['xpert', 'cursor', 'keyboard', 'cursor', 'key', 'mous', 'avail', 'hint', 'welcom', 'thank']\n"
     ]
    }
   ],
   "source": [
    "doc_sample = newsgroups_all.data[0]\n",
    "print('Original document: ')\n",
    "print(doc_sample)\n",
    "\n",
    "print('\\n\\nTokenized document: ')\n",
    "words = []\n",
    "for token in gensim.utils.tokenize(doc_sample):\n",
    "    words.append(token)\n",
    "print(words)\n",
    "\n",
    "print('\\n\\nPreprocessed document: ')\n",
    "print(preprocess(doc_sample))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How do the first 10 look like?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\txpert, cursor, keyboard, cursor, key, mous, avail, hint, welcom, thank\n",
      "1\tobtain, copi, open, look, widget, obtain, need, order, copi, thank\n",
      "2\tright, signal, strong, live, west, philadelphia, perfect, sport, fan, dream\n",
      "3\tcanadian, thing, coach, boston, bruin, colorado, rocki, summari, post, gather\n",
      "4\theck, feel, like, time, includ, cafeteria, work, half, time, headach\n",
      "5\tdamn, right, late, climb, meet, morn, bother, right, foot, asleep\n",
      "6\tolympus, stylus, pocket, camera, smallest, class, includ, time, date, stamp\n",
      "7\tinclud, follow, chmos, clock, generat, driver, processor, chmos, eras, prom\n",
      "8\tchang, intel, discov, xclient, xload, longer, work, bomb, messag, error\n",
      "9\ttermin, like, power, server, run, window, manag, special, client, program\n"
     ]
    }
   ],
   "source": [
    "for i in range(0, 10):\n",
    "    print(str(i) + \"\\t\" + \", \".join(preprocess(newsgroups_all.data[i])[:10]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's represent each document as a dictionary of relevant words. Each word (*value* in the dictionary) has a unique identifier (*key*): "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9850\n",
      "39350\n",
      "0 avail\n",
      "1 cursor\n",
      "2 hint\n",
      "3 key\n",
      "4 keyboard\n",
      "5 mous\n",
      "6 thank\n",
      "7 welcom\n",
      "8 xpert\n",
      "9 copi\n"
     ]
    }
   ],
   "source": [
    "processed_docs = []\n",
    "for i in range(0, len(newsgroups_all.data)):\n",
    "    processed_docs.append(preprocess(newsgroups_all.data[i]))\n",
    "\n",
    "print(len(processed_docs))\n",
    "    \n",
    "dictionary = gensim.corpora.Dictionary(processed_docs)\n",
    "print(len(dictionary))\n",
    "\n",
    "index = 0\n",
    "for key, value in dictionary.iteritems():\n",
    "    print(key, value)\n",
    "    index += 1\n",
    "    if index > 9:\n",
    "        break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Put some contraints on the dictionary of terms: for instance, keep up to $100,000$ words that occur more frequently than $10$ times (`no_below`) and less frequently than in $50\\%$ of the documents (`no_above`). This should help you extract the most useful terms, while still keeping a reasonable number of them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5868\n"
     ]
    }
   ],
   "source": [
    "dictionary.filter_extremes(no_below=10, no_above=0.5, keep_n=100000)\n",
    "print(len(dictionary))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see how a particular document is represented in this dictionary: for example, look into the very first post, or into the 100th:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0, 1), (1, 2), (2, 1), (3, 1), (4, 1), (5, 1), (6, 1), (7, 1), (8, 1)]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bow_corpus = [dictionary.doc2bow(doc) for doc in processed_docs]\n",
    "bow_corpus[0]\n",
    "#bow_corpus[99]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's decode what each index (key) in this dictionary points to:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Key 0 =\"avail\":    occurrences=1\n",
      "Key 1 =\"cursor\":    occurrences=2\n",
      "Key 2 =\"hint\":    occurrences=1\n",
      "Key 3 =\"key\":    occurrences=1\n",
      "Key 4 =\"keyboard\":    occurrences=1\n",
      "Key 5 =\"mous\":    occurrences=1\n",
      "Key 6 =\"thank\":    occurrences=1\n",
      "Key 7 =\"welcom\":    occurrences=1\n",
      "Key 8 =\"xpert\":    occurrences=1\n"
     ]
    }
   ],
   "source": [
    "#bow_doc = bow_corpus[99]\n",
    "bow_doc = bow_corpus[0]\n",
    "\n",
    "for i in range(len(bow_doc)):\n",
    "    print(f\"Key {bow_doc[i][0]} =\\\"{dictionary[bow_doc[i][0]]}\\\":\\\n",
    "    occurrences={bow_doc[i][1]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train an LDA model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Topic: 0 \n",
      "Words: 0.021*\"encrypt\" + 0.018*\"secur\" + 0.018*\"chip\" + 0.016*\"govern\" + 0.013*\"clipper\" + 0.012*\"public\" + 0.010*\"privaci\" + 0.010*\"key\" + 0.010*\"phone\" + 0.009*\"algorithm\"\n",
      "Topic: 1 \n",
      "Words: 0.017*\"appear\" + 0.014*\"copi\" + 0.013*\"cover\" + 0.013*\"star\" + 0.013*\"book\" + 0.011*\"penalti\" + 0.010*\"black\" + 0.009*\"comic\" + 0.008*\"blue\" + 0.008*\"green\"\n",
      "Topic: 2 \n",
      "Words: 0.031*\"window\" + 0.015*\"server\" + 0.012*\"program\" + 0.012*\"file\" + 0.012*\"applic\" + 0.012*\"display\" + 0.011*\"widget\" + 0.010*\"version\" + 0.010*\"motif\" + 0.010*\"support\"\n",
      "Topic: 3 \n",
      "Words: 0.015*\"space\" + 0.007*\"launch\" + 0.007*\"year\" + 0.007*\"medic\" + 0.006*\"patient\" + 0.006*\"orbit\" + 0.006*\"research\" + 0.006*\"diseas\" + 0.005*\"develop\" + 0.005*\"nasa\"\n",
      "Topic: 4 \n",
      "Words: 0.018*\"armenian\" + 0.011*\"peopl\" + 0.008*\"kill\" + 0.008*\"said\" + 0.007*\"turkish\" + 0.006*\"muslim\" + 0.006*\"jew\" + 0.006*\"govern\" + 0.005*\"state\" + 0.005*\"greek\"\n",
      "Topic: 5 \n",
      "Words: 0.024*\"price\" + 0.021*\"sale\" + 0.020*\"offer\" + 0.017*\"drive\" + 0.017*\"sell\" + 0.016*\"includ\" + 0.013*\"ship\" + 0.013*\"interest\" + 0.011*\"ask\" + 0.010*\"condit\"\n",
      "Topic: 6 \n",
      "Words: 0.018*\"mail\" + 0.016*\"list\" + 0.015*\"file\" + 0.015*\"inform\" + 0.013*\"send\" + 0.012*\"post\" + 0.012*\"avail\" + 0.010*\"request\" + 0.010*\"program\" + 0.009*\"includ\"\n",
      "Topic: 7 \n",
      "Words: 0.019*\"like\" + 0.016*\"know\" + 0.011*\"time\" + 0.011*\"look\" + 0.010*\"think\" + 0.008*\"want\" + 0.008*\"thing\" + 0.008*\"good\" + 0.007*\"go\" + 0.007*\"bike\"\n",
      "Topic: 8 \n",
      "Words: 0.033*\"game\" + 0.022*\"team\" + 0.017*\"play\" + 0.015*\"year\" + 0.013*\"player\" + 0.011*\"season\" + 0.008*\"hockey\" + 0.008*\"score\" + 0.007*\"leagu\" + 0.007*\"goal\"\n",
      "Topic: 9 \n",
      "Words: 0.013*\"peopl\" + 0.012*\"think\" + 0.011*\"like\" + 0.009*\"time\" + 0.009*\"right\" + 0.009*\"israel\" + 0.009*\"know\" + 0.006*\"reason\" + 0.006*\"point\" + 0.006*\"thing\"\n"
     ]
    }
   ],
   "source": [
    "# Create the dictionary\n",
    "id2word = dictionary\n",
    "\n",
    "# Create the corpus with word frequencies\n",
    "corpus = bow_corpus\n",
    "\n",
    "# Build the LDA model\n",
    "lda_model = gensim.models.ldamodel.LdaModel(corpus=corpus,\n",
    "                                           id2word=id2word,\n",
    "                                           num_topics=10, \n",
    "                                           random_state=100,\n",
    "                                           update_every=1,\n",
    "                                           chunksize=1000,\n",
    "                                           passes=10,\n",
    "                                           alpha='symmetric',\n",
    "                                           iterations=100,\n",
    "                                           per_word_topics=True)\n",
    "\n",
    "\n",
    "for index, topic in lda_model.print_topics(-1):\n",
    "    print(f\"Topic: {index} \\nWords: {topic}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interpret the results\n",
    "\n",
    "What is the most representative topic in each document?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ID  Main Topic  Contribution (%)  Keywords                                Snippet                                                                           \n",
      " 0   2           0.8268            window, server, program, file, applic\n",
      "  ['xpert', 'cursor', 'keyboard', 'cursor', 'key', 'mous', 'avail', 'hint']         \n",
      " 1   6           0.4745            mail, list, file, inform, send\n",
      "         ['obtain', 'copi', 'open', 'look', 'widget', 'obtain', 'need', 'order']           \n",
      " 2   7           0.4229            like, know, time, look, think\n",
      "          ['right', 'signal', 'strong', 'live', 'west', 'philadelphia', 'perfect', 'sport'] \n",
      " 3   8           0.4159            game, team, play, year, player\n",
      "         ['canadian', 'thing', 'coach', 'boston', 'bruin', 'colorado', 'rocki', 'summari'] \n",
      " 4   9           0.9039            peopl, think, like, time, right\n",
      "        ['heck', 'feel', 'like', 'time', 'includ', 'cafeteria', 'work', 'half']           \n",
      " 5   7           0.6291            like, know, time, look, think\n",
      "          ['damn', 'right', 'late', 'climb', 'meet', 'morn', 'bother', 'right']             \n",
      " 6   3           0.3483            space, launch, year, medic, patient\n",
      "    ['olympus', 'stylus', 'pocket', 'camera', 'smallest', 'class', 'includ', 'time']  \n",
      " 7   5           0.3798            price, sale, offer, drive, sell\n",
      "        ['includ', 'follow', 'chmos', 'clock', 'generat', 'driver', 'processor', 'chmos'] \n",
      " 8   2           0.7943            window, server, program, file, applic\n",
      "  ['chang', 'intel', 'discov', 'xclient', 'xload', 'longer', 'work', 'bomb']        \n",
      " 9   2           0.6384            window, server, program, file, applic\n",
      "  ['termin', 'like', 'power', 'server', 'run', 'window', 'manag', 'special']        \n"
     ]
    }
   ],
   "source": [
    "def analyse_topics(ldamodel, corpus, texts):\n",
    "    main_topic = {}\n",
    "    percentage = {}\n",
    "    keywords = {}\n",
    "    text_snippets = {}\n",
    "    # Get main topic in each document\n",
    "    for i, topic_list in enumerate(ldamodel[corpus]):\n",
    "        #print(\"\\n\")\n",
    "        #print(topic_list)\n",
    "        #print(\"\\n\")\n",
    "        #for i in range(0, len(topic_list)):\n",
    "        #    print (topic_list[i])\n",
    "        topic = topic_list[0] if ldamodel.per_word_topics else topic_list            \n",
    "        #print(topic)\n",
    "        topic = sorted(topic, key=lambda x: (x[1]), reverse=True)\n",
    "        # Get the main topic, contribution (%) and keywords for each document\n",
    "        for j, (topic_num, prop_topic) in enumerate(topic):\n",
    "            if j == 0:  # => dominant topic\n",
    "                wp = ldamodel.show_topic(topic_num)\n",
    "                topic_keywords = \", \".join([word for word, prop in wp[:5]])\n",
    "                main_topic[i] = int(topic_num)\n",
    "                percentage[i] = round(prop_topic,4)\n",
    "                keywords[i] = topic_keywords\n",
    "                text_snippets[i] = texts[i][:8]\n",
    "            else:\n",
    "                break\n",
    "    return main_topic, percentage, keywords, text_snippets\n",
    "\n",
    "\n",
    "main_topic, percentage, keywords, text_snippets = analyse_topics(\n",
    "    lda_model, bow_corpus, processed_docs)\n",
    "\n",
    "indexes = []\n",
    "rows = []\n",
    "for i in range(0, 10):\n",
    "    indexes.append(i)\n",
    "rows.append(['ID', 'Main Topic', 'Contribution (%)', 'Keywords', 'Snippet'])\n",
    "\n",
    "for idx in indexes:\n",
    "    rows.append([str(idx), f\"{main_topic.get(idx)}\", \n",
    "                f\"{percentage.get(idx):.4f}\",\n",
    "                f\"{keywords.get(idx)}\\n\",\n",
    "                f\"{text_snippets.get(idx)}\"])\n",
    "\n",
    "columns = zip(*rows)\n",
    "column_widths = [max(len(item) for item in col) for col in columns]\n",
    "for row in rows:\n",
    "    print(''.join(' {:{width}} '.format(row[i], width=column_widths[i]) \n",
    "                  for i in range(0, len(row)))) \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explore words and topics with pyLDAvis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ekaterinakochmar/opt/anaconda3/lib/python3.7/site-packages/sklearn/decomposition/_online_lda.py:29: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  EPS = np.finfo(np.float).eps\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_el861121403549861152807623287701\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "\n",
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\"window\", \"window\", \"winnipeg\", \"wiretap\", \"wolverin\", \"women\", \"women\", \"women\", \"word\", \"word\", \"word\", \"word\", \"word\", \"word\", \"word\", \"word\", \"work\", \"work\", \"work\", \"work\", \"work\", \"work\", \"work\", \"work\", \"work\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"wound\", \"xfree\", \"xlib\", \"xterm\", \"xview\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"yeast\", \"zionist\"]}, \"R\": 30, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [10, 8, 5, 4, 9, 7, 3, 1, 6, 2]};\n",
       "\n",
       "function LDAvis_load_lib(url, callback){\n",
       "  var s = document.createElement('script');\n",
       "  s.src = url;\n",
       "  s.async = true;\n",
       "  s.onreadystatechange = s.onload = callback;\n",
       "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
       "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "}\n",
       "\n",
       "if(typeof(LDAvis) !== \"undefined\"){\n",
       "   // already loaded: just create the visualization\n",
       "   !function(LDAvis){\n",
       "       new LDAvis(\"#\" + \"ldavis_el861121403549861152807623287701\", ldavis_el861121403549861152807623287701_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_el861121403549861152807623287701\", ldavis_el861121403549861152807623287701_data);\n",
       "      });\n",
       "    });\n",
       "}else{\n",
       "    // require.js not available: dynamically load d3 & LDAvis\n",
       "    LDAvis_load_lib(\"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min.js\", function(){\n",
       "         LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
       "                 new LDAvis(\"#\" + \"ldavis_el861121403549861152807623287701\", ldavis_el861121403549861152807623287701_data);\n",
       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "PreparedData(topic_coordinates=              x         y  topics  cluster       Freq\n",
       "topic                                                \n",
       "9     -0.071274 -0.158253       1        1  16.068928\n",
       "7      0.021450 -0.150021       2        1  15.559736\n",
       "4      0.043560 -0.124907       3        1  14.497130\n",
       "3     -0.061426  0.004703       4        1  12.563197\n",
       "8      0.209376 -0.183576       5        1   9.811371\n",
       "6     -0.159978  0.164846       6        1   9.369773\n",
       "2     -0.185676  0.098406       7        1   7.510983\n",
       "0     -0.170308 -0.041953       8        1   6.918313\n",
       "5      0.083248  0.193076       9        1   4.628499\n",
       "1      0.291029  0.197679      10        1   3.072072, topic_info=          Term         Freq        Total Category  logprob  loglift\n",
       "123       game  2208.000000  2208.000000  Default  30.0000  30.0000\n",
       "273     window  1629.000000  1629.000000  Default  29.0000  29.0000\n",
       "598   armenian  1712.000000  1712.000000  Default  28.0000  28.0000\n",
       "86        team  1451.000000  1451.000000  Default  27.0000  27.0000\n",
       "254       mail  1397.000000  1397.000000  Default  26.0000  26.0000\n",
       "...        ...          ...          ...      ...      ...      ...\n",
       "311       forc   152.136932   683.506583  Topic10  -4.8726   1.9804\n",
       "693      white    98.440851   321.459097  Topic10  -5.3079   2.2994\n",
       "468       issu   108.343581   724.233607  Topic10  -5.2120   1.5830\n",
       "130       left    99.866881   792.428832  Topic10  -5.2935   1.4116\n",
       "1087     earth    90.663874   416.671383  Topic10  -5.3902   1.9577\n",
       "\n",
       "[648 rows x 6 columns], token_table=      Topic      Freq     Term\n",
       "term                          \n",
       "1713      2  0.994027    accid\n",
       "97        1  0.590655   actual\n",
       "97        2  0.242546   actual\n",
       "97        4  0.002513   actual\n",
       "97        5  0.049012   actual\n",
       "...     ...       ...      ...\n",
       "156       6  0.034544     year\n",
       "156       8  0.002348     year\n",
       "156       9  0.040581     year\n",
       "2740      4  0.991179    yeast\n",
       "5438      1  0.990155  zionist\n",
       "\n",
       "[1525 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[10, 8, 5, 4, 9, 7, 3, 1, 6, 2])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pyLDAvis.gensim\n",
    "pyLDAvis.enable_notebook()\n",
    "vis = pyLDAvis.gensim.prepare(lda_model, corpus, dictionary=lda_model.id2word)\n",
    "vis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note, for newer versions of `gensim`, use the following code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import pyLDAvis.gensim_models\n",
    "#pyLDAvis.enable_notebook()\n",
    "#vis = pyLDAvis.gensim_models.prepare(lda_model, corpus, dictionary=lda_model.id2word)\n",
    "#vis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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    "version": 3
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