Gensim_topic_modeling.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "pvHQQRVnSjwT"
   },
   "outputs": [],
   "source": [
    "# import modules\n",
    "import pandas as pd\n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns \n",
    "from sklearn.datasets import fetch_20newsgroups\n",
    "import nltk\n",
    "import gensim  \n",
    "from gensim import models \n",
    "from gensim.corpora.dictionary import Dictionary \n",
    "from gensim.models import CoherenceModel \n",
    "from gensim import corpora \n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "from gensim.models import Phrases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ydvrPywjkAli",
    "outputId": "94e49f80-d349-4427-d62e-fe816bc33805"
   },
   "outputs": [],
   "source": [
    "nltk.download('stopwords')\n",
    "nltk.download('wordnet')\n",
    "nltk.download('omw-1.4')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "fyK-A5F0n9J9",
    "outputId": "18f9e664-22bf-406b-809f-943054b28a93"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11314"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read the text data\n",
    "newsgroups_train = fetch_20newsgroups(subset='train').data\n",
    "len(newsgroups_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Fb4jlpWWio4F",
    "outputId": "428f115c-a4c7-40dc-d39e-d865623a3b3f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7532"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newsgroups_test = fetch_20newsgroups(subset='test').data\n",
    "len(newsgroups_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "sQ7c07GulIs6",
    "outputId": "59b109e2-c4da-4894-a856-d0f450e5aa36"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[\"From: lerxst@wam.umd.edu (where's my thing)\\nSubject: WHAT car is this!?\\nNntp-Posting-Host: rac3.wam.umd.edu\\nOrganization: University of Maryland, College Park\\nLines: 15\\n\\n I was wondering if anyone out there could enlighten me on this car I saw\\nthe other day. It was a 2-door sports car, looked to be from the late 60s/\\nearly 70s. It was called a Bricklin. The doors were really small. In addition,\\nthe front bumper was separate from the rest of the body. This is \\nall I know. If anyone can tellme a model name, engine specs, years\\nof production, where this car is made, history, or whatever info you\\nhave on this funky looking car, please e-mail.\\n\\nThanks,\\n- IL\\n   ---- brought to you by your neighborhood Lerxst ----\\n\\n\\n\\n\\n\",\n",
       " \"From: guykuo@carson.u.washington.edu (Guy Kuo)\\nSubject: SI Clock Poll - Final Call\\nSummary: Final call for SI clock reports\\nKeywords: SI,acceleration,clock,upgrade\\nArticle-I.D.: shelley.1qvfo9INNc3s\\nOrganization: University of Washington\\nLines: 11\\nNNTP-Posting-Host: carson.u.washington.edu\\n\\nA fair number of brave souls who upgraded their SI clock oscillator have\\nshared their experiences for this poll. Please send a brief message detailing\\nyour experiences with the procedure. Top speed attained, CPU rated speed,\\nadd on cards and adapters, heat sinks, hour of usage per day, floppy disk\\nfunctionality with 800 and 1.4 m floppies are especially requested.\\n\\nI will be summarizing in the next two days, so please add to the network\\nknowledge base if you have done the clock upgrade and haven't answered this\\npoll. Thanks.\\n\\nGuy Kuo <guykuo@u.washington.edu>\\n\"]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the text data\n",
    "newsgroups_train[:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IARnK3JI9HXV"
   },
   "source": [
    "# Tokenize data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "YCbtq4JXlIr6"
   },
   "outputs": [],
   "source": [
    "# tokenize data \n",
    "word_tokenize = \\\n",
    "[gensim.utils.simple_preprocess(word, min_len=3) for word in newsgroups_train]\n",
    "# keep only alphabetical characters\n",
    "clean_tokens = \\\n",
    "[[word for word in doc if word.isalpha()] for doc in word_tokenize]\n",
    "# remove stop words\n",
    "no_stops = \\\n",
    "[[t for t in doc if t not in stopwords.words(\"english\")] for doc in clean_tokens]\n",
    "# instantiate WordNetLemmatizer\n",
    "lem = WordNetLemmatizer()\n",
    "# lemmatize tokens \n",
    "lemmatized = [[lem.lemmatize(t) for t in doc] for doc in no_stops]\n",
    "# compute bigrams \n",
    "bigram = Phrases(lemmatized, min_count=20, threshold=100)\n",
    "trigram = Phrases(bigram[lemmatized], threshold=100)\n",
    "for ind, elem in enumerate(lemmatized):\n",
    "  for token in bigram[lemmatized][ind]:\n",
    "    if \"_\" in token:\n",
    "      lemmatized[ind].append(token)\n",
    "  for token in trigram[lemmatized][ind]:\n",
    "    if \"_\" in token:\n",
    "      lemmatized[ind].append(token)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XcFPSiG69QY9"
   },
   "source": [
    "# Create corpus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ASGPf0LC4lKu"
   },
   "outputs": [],
   "source": [
    "# create dictionary\n",
    "dictionary = corpora.Dictionary(lemmatized)\n",
    "# filter out non frequent words and too frequent words \n",
    "dictionary.filter_extremes(no_below=20, no_above=.5)\n",
    "corpus = [dictionary.doc2bow(text) for text in lemmatized]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uyM_PZWl9eN4"
   },
   "source": [
    "# Number of topics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "8RqxrHoc9h1u"
   },
   "outputs": [],
   "source": [
    "# compute coherence value for different topic numbers\n",
    "cherence_lda = []\n",
    "for v in range(10, 25, 1):\n",
    "  ldamodel = models.LdaModel(corpus, \n",
    "                             num_topics=v, \n",
    "                             id2word=dictionary, \n",
    "                             passes=100)\n",
    "  coherence_model = \\\n",
    "  CoherenceModel(model=ldamodel, texts=lemmatized, \n",
    "                 dictionary=dictionary, coherence='c_v')\n",
    "  coherence_value = coherence_model.get_coherence()\n",
    "  cherence_lda.append(coherence_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 350
    },
    "id": "e3hksYwg-qiJ",
    "outputId": "4286c6ff-6e56-492f-d053-21efa6174518"
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize coherence values vs number of topics\n",
    "plt.figure(figsize=(8, 5))\n",
    "sns.pointplot(x=list(range(10, 25, 1)), y=cherence_lda)\n",
    "plt.xlabel(\"Number of topics\")\n",
    "plt.ylabel(\"Coherence value\")\n",
    "plt.title(\"Coherence vs topic number\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "N0TrNUeRzcgy"
   },
   "source": [
    "We can clearly see that the model with 19 topics has the highest Coherence score. Train the model with 19 topics."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1MMGGO7l-TGu"
   },
   "source": [
    "# LDA model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "28Bo6kj08pD2"
   },
   "outputs": [],
   "source": [
    "# LDA model with 19 topics\n",
    "ldamodel = models.LdaModel(corpus, \n",
    "                           num_topics=19, \n",
    "                           id2word=dictionary, \n",
    "                           passes=50, \n",
    "                           eval_every=None, \n",
    "                           alpha='auto', \n",
    "                           eta='auto', \n",
    "                           chunksize=1000, \n",
    "                           iterations=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "NcnjpK6O_fKG",
    "outputId": "58d890e8-864b-4277-c591-9cb197f6bfb6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0,\n",
       "  '0.045*\"space\" + 0.038*\"nasa\" + 0.035*\"gov\" + 0.013*\"jpl_nasa\" + 0.010*\"local_bus\" + 0.010*\"space_shuttle\" + 0.010*\"launch\" + 0.010*\"moon\" + 0.010*\"prism_gatech\" + 0.009*\"henry\" + 0.009*\"jpl\" + 0.009*\"article\" + 0.009*\"shuttle\" + 0.009*\"orbit\" + 0.008*\"space_station\"'),\n",
       " (1,\n",
       "  '0.024*\"andrew_cmu\" + 0.019*\"hockey\" + 0.018*\"uiuc\" + 0.018*\"andrew\" + 0.016*\"university_illinois\" + 0.015*\"article\" + 0.014*\"university\" + 0.013*\"cmu\" + 0.011*\"cso\" + 0.010*\"colorado\" + 0.008*\"frank\" + 0.008*\"illinois\" + 0.008*\"cso_uiuc\" + 0.007*\"carnegie_mellon\" + 0.007*\"sgi\"'),\n",
       " (2,\n",
       "  '0.014*\"wire\" + 0.013*\"power\" + 0.011*\"purdue\" + 0.011*\"ecn_purdue\" + 0.011*\"ground\" + 0.010*\"circuit\" + 0.010*\"power_supply\" + 0.009*\"signal\" + 0.009*\"input\" + 0.008*\"mil\" + 0.008*\"university\" + 0.008*\"navy\" + 0.008*\"supply\" + 0.008*\"get_rid\" + 0.008*\"use\"'),\n",
       " (3,\n",
       "  '0.023*\"armenian\" + 0.014*\"turkish\" + 0.011*\"war\" + 0.010*\"stanley_cup\" + 0.010*\"people\" + 0.009*\"greek\" + 0.008*\"turk\" + 0.007*\"freenet_carleton\" + 0.007*\"turkey\" + 0.007*\"said\" + 0.007*\"armenia\" + 0.007*\"jew\" + 0.006*\"serdar_argic\" + 0.006*\"nazi\" + 0.006*\"world\"'),\n",
       " (4,\n",
       "  '0.214*\"max\" + 0.026*\"video_card\" + 0.024*\"access_unix\" + 0.018*\"clayton_cramer\" + 0.018*\"rochester\" + 0.017*\"gay\" + 0.016*\"homosexual\" + 0.015*\"iastate\" + 0.014*\"cramer\" + 0.013*\"virginia\" + 0.012*\"cramer_optilink\" + 0.011*\"com\" + 0.011*\"optilink\" + 0.011*\"marriage\" + 0.010*\"simms\"'),\n",
       " (5,\n",
       "  '0.026*\"new_york\" + 0.019*\"united_state\" + 0.016*\"new\" + 0.015*\"year\" + 0.013*\"president\" + 0.012*\"state\" + 0.011*\"american\" + 0.010*\"april\" + 0.010*\"york\" + 0.007*\"university\" + 0.007*\"united\" + 0.007*\"city\" + 0.006*\"program\" + 0.006*\"press\" + 0.006*\"national\"'),\n",
       " (6,\n",
       "  '0.013*\"key\" + 0.010*\"gun\" + 0.010*\"government\" + 0.009*\"law\" + 0.006*\"law_enforcement\" + 0.006*\"clipper_chip\" + 0.006*\"public\" + 0.006*\"state\" + 0.006*\"right\" + 0.006*\"chip\" + 0.006*\"com\" + 0.006*\"system\" + 0.006*\"use\" + 0.006*\"encryption\" + 0.005*\"serial_number\"'),\n",
       " (7,\n",
       "  '0.108*\"nntp_posting\" + 0.042*\"posting\" + 0.038*\"host\" + 0.036*\"nntp\" + 0.029*\"com\" + 0.024*\"university\" + 0.020*\"distribution_world\" + 0.015*\"article\" + 0.012*\"distribution\" + 0.012*\"newsreader_tin\" + 0.011*\"thanks\" + 0.010*\"anyone\" + 0.010*\"reply\" + 0.010*\"computer_science\" + 0.009*\"apr\"'),\n",
       " (8,\n",
       "  '0.022*\"com\" + 0.010*\"health\" + 0.009*\"netcom\" + 0.008*\"article\" + 0.007*\"child\" + 0.007*\"use\" + 0.007*\"health_care\" + 0.007*\"insurance\" + 0.007*\"medical\" + 0.007*\"disease\" + 0.007*\"service\" + 0.006*\"communication_service\" + 0.006*\"patient\" + 0.005*\"study\" + 0.005*\"case\"'),\n",
       " (9,\n",
       "  '0.016*\"would\" + 0.014*\"one\" + 0.012*\"like\" + 0.011*\"get\" + 0.010*\"think\" + 0.010*\"time\" + 0.010*\"know\" + 0.009*\"people\" + 0.007*\"good\" + 0.007*\"thing\" + 0.007*\"well\" + 0.007*\"say\" + 0.006*\"make\" + 0.006*\"much\" + 0.006*\"year\"'),\n",
       " (10,\n",
       "  '0.037*\"game\" + 0.033*\"team\" + 0.019*\"player\" + 0.015*\"play\" + 0.014*\"win\" + 0.013*\"season\" + 0.012*\"los_angeles\" + 0.011*\"year\" + 0.010*\"league\" + 0.009*\"san_jose\" + 0.009*\"san_francisco\" + 0.009*\"nhl\" + 0.009*\"caltech\" + 0.009*\"keith\" + 0.008*\"goal\"'),\n",
       " (11,\n",
       "  '0.023*\"god\" + 0.011*\"christian\" + 0.011*\"one\" + 0.011*\"people\" + 0.008*\"jesus\" + 0.008*\"say\" + 0.007*\"believe\" + 0.007*\"would\" + 0.006*\"bible\" + 0.006*\"evidence\" + 0.006*\"religion\" + 0.005*\"life\" + 0.005*\"think\" + 0.005*\"church\" + 0.005*\"question\"'),\n",
       " (12,\n",
       "  '0.031*\"com\" + 0.027*\"bike\" + 0.026*\"cleveland_freenet\" + 0.019*\"dod\" + 0.014*\"article\" + 0.012*\"msg\" + 0.012*\"dog\" + 0.011*\"apr\" + 0.011*\"motorcycle\" + 0.010*\"bnr\" + 0.010*\"thu_apr\" + 0.010*\"ride\" + 0.009*\"freenet\" + 0.009*\"score\" + 0.008*\"rec\"'),\n",
       " (13,\n",
       "  '0.044*\"distribution_usa\" + 0.026*\"car\" + 0.019*\"sale\" + 0.017*\"usa\" + 0.015*\"new\" + 0.014*\"distribution\" + 0.014*\"price\" + 0.014*\"com\" + 0.009*\"nasa_gov\" + 0.009*\"engine\" + 0.008*\"offer\" + 0.008*\"hewlett_packard\" + 0.007*\"sell\" + 0.007*\"vax\" + 0.007*\"dealer\"'),\n",
       " (14,\n",
       "  '0.019*\"drive\" + 0.011*\"card\" + 0.011*\"system\" + 0.010*\"problem\" + 0.009*\"bit\" + 0.009*\"com\" + 0.008*\"use\" + 0.008*\"driver\" + 0.007*\"disk\" + 0.007*\"work\" + 0.006*\"mac\" + 0.006*\"access_digex\" + 0.006*\"one\" + 0.006*\"access\" + 0.005*\"color\"'),\n",
       " (15,\n",
       "  '0.064*\"com\" + 0.025*\"sun\" + 0.023*\"ibm\" + 0.018*\"opinion_expressed\" + 0.017*\"austin_ibm\" + 0.015*\"article\" + 0.015*\"utexas\" + 0.014*\"austin\" + 0.013*\"midway_uchicago\" + 0.012*\"informatik_uni\" + 0.012*\"reply\" + 0.012*\"uni\" + 0.011*\"uchicago\" + 0.011*\"standard_disclaimer\" + 0.011*\"east_sun\"'),\n",
       " (16,\n",
       "  '0.031*\"apr_gmt\" + 0.030*\"israel\" + 0.025*\"israeli\" + 0.016*\"arab\" + 0.016*\"apr\" + 0.015*\"cwru\" + 0.013*\"san_diego\" + 0.011*\"case_western\" + 0.011*\"article\" + 0.011*\"cleveland\" + 0.011*\"reserve_university\" + 0.011*\"gmt\" + 0.010*\"athena_mit\" + 0.009*\"case\" + 0.008*\"public_domain\"'),\n",
       " (17,\n",
       "  '0.009*\"window_manager\" + 0.008*\"motif\" + 0.008*\"com\" + 0.007*\"widget\" + 0.007*\"mit\" + 0.006*\"function\" + 0.006*\"system\" + 0.006*\"manager\" + 0.006*\"event\" + 0.006*\"byte\" + 0.005*\"stratus\" + 0.005*\"satellite\" + 0.004*\"columbia\" + 0.004*\"earth\" + 0.004*\"vice_president\"'),\n",
       " (18,\n",
       "  '0.026*\"file\" + 0.023*\"window\" + 0.016*\"program\" + 0.010*\"available\" + 0.010*\"use\" + 0.009*\"image\" + 0.008*\"information\" + 0.008*\"version\" + 0.008*\"server\" + 0.008*\"user\" + 0.007*\"software\" + 0.007*\"source\" + 0.007*\"ftp\" + 0.007*\"graphic\" + 0.007*\"also\"')]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print topics\n",
    "ldamodel.print_topics(num_words=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HcmUvFY28K-s"
   },
   "source": [
    "# Assign topics to the original data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "gvikVK1d8R2M"
   },
   "outputs": [],
   "source": [
    "# create DF with topics and probability scores for each text\n",
    "topic_details = pd.DataFrame()\n",
    "for i, row in enumerate(ldamodel[corpus]):\n",
    "  row = sorted(row, key=lambda x: (x[1]), reverse=True)\n",
    "  for j, (topic_num, prop_topic) in enumerate(row):\n",
    "    if j==0:\n",
    "      topic_details = topic_details.append(\n",
    "          pd.Series([topic_num, prop_topic]), ignore_index=True\n",
    "      )\n",
    "topic_details.columns=[\"Topics\", \"% Score\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "3v7cq97kAN8E"
   },
   "outputs": [],
   "source": [
    "# assign tokens and original texts to the topics\n",
    "topic_details.loc[:, 'Tokens'] = np.array(lemmatized, dtype='object')\n",
    "topic_details.loc[:, 'Original text'] = newsgroups_train"
   ]
  },
  {
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   "execution_count": null,
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    "id": "AhP-v2ZtAz6T",
    "outputId": "cb62da3c-a108-4357-d4a7-f4f470335fdc"
   },
   "outputs": [
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      "text/plain": [
       "    Topics   % Score                                             Tokens  \\\n",
       "40     9.0  0.356886  [sean, garrison, subject, bonilla, nntp, posti...   \n",
       "41     7.0  0.510100  [root, ncube, com, operator, subject, fax, mod...   \n",
       "42    14.0  0.367355  [cleveland, freenet, edu, sam, latonia, subjec...   \n",
       "43     9.0  0.290688  [paul, csd, csd, uwm, edu, paul, krueger, subj...   \n",
       "44     9.0  0.313854  [cmeyer, bloch, stanford, edu, craig, meyer, s...   \n",
       "45     1.0  0.425725  [robert, everett, brunskill, andrew, cmu, edu,...   \n",
       "\n",
       "                                        Original text  \n",
       "40  From:  (Sean Garrison)\\nSubject: Re: Bonilla\\n...  \n",
       "41  From: root@ncube.com (Operator)\\nSubject: Re: ...  \n",
       "42  From: ab245@cleveland.Freenet.Edu (Sam Latonia...  \n",
       "43  From: paul@csd4.csd.uwm.edu (Paul R Krueger)\\n...  \n",
       "44  From: cmeyer@bloch.Stanford.EDU (Craig Meyer)\\...  \n",
       "45  From: Robert Everett Brunskill <rb6t+@andrew.c...  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
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    }
   ],
   "source": [
    "topic_details.loc[40:45]"
   ]
  }
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