Topic_Modeling_with_LSA_LDA .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Topic Modeling with LSA, LDA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's start by loading the data of the 20 newsgroups dataset in scikit-learn. You can use all the data but for simpler and fast execution, the code below selects first 100 articles.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100\n",
      "A fair number of brave souls who upgraded their SI clock oscillator have\n",
      "shared their experiences for this poll. Please send a brief message detailing\n",
      "your experiences with the procedure. Top speed attained, CPU rated speed,\n",
      "add on cards and adapters, heat sinks, hour of usage per day, floppy disk\n",
      "functionality with 800 and 1.4 m floppies are especially requested.\n",
      "\n",
      "I will be summarizing in the next two days, so please add to the network\n",
      "knowledge base if you have done the clock upgrade and haven't answered this\n",
      "poll. Thanks.\n",
      "\n",
      "<><><><>><><><>><><><><><>\n",
      "\n",
      "well folks, my mac plus finally gave up the ghost this weekend after\n",
      "starting life as a 512k way back in 1985.  sooo, i'm in the market for a\n",
      "new machine a bit sooner than i intended to be...\n",
      "\n",
      "i'm looking into picking up a powerbook 160 or maybe 180 and have a bunch\n",
      "of questions that (hopefully) somebody can answer:\n",
      "\n",
      "* does anybody know any dirt on when the next round of powerbook\n",
      "introductions are expected?  i'd heard the 185c was supposed to make an\n",
      "appearence \"this summer\" but haven't heard anymore on it - and since i\n",
      "don't have access to macleak, i was wondering if anybody out there had\n",
      "more info...\n",
      "\n",
      "* has anybody heard rumors about price drops to the powerbook line like the\n",
      "ones the duo's just went through recently?\n",
      "\n",
      "* what's the impression of the display on the 180?  i could probably swing\n",
      "a 180 if i got the 80Mb disk rather than the 120, but i don't really have\n",
      "a feel for how much \"better\" the display is (yea, it looks great in the\n",
      "store, but is that all \"wow\" or is it really that good?).  could i solicit\n",
      "some opinions of people who use the 160 and 180 day-to-day on if its worth\n",
      "taking the disk size and money hit to get the active display?  (i realize\n",
      "this is a real subjective question, but i've only played around with the\n",
      "machines in a computer store breifly and figured the opinions of somebody\n",
      "who actually uses the machine daily might prove helpful).\n",
      "\n",
      "* how well does hellcats perform?  ;)\n",
      "\n",
      "thanks a bunch in advance for any info - if you could email, i'll post a\n",
      "summary (news reading time is at a premium with finals just around the\n",
      "corner... :( )\n",
      "--\n",
      "Tom Willis  \\  twillis@ecn.purdue.edu    \\    Purdue Electrical Engineering\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import fetch_20newsgroups\n",
    "dataset = fetch_20newsgroups(remove=('headers', 'footers', 'quotes'))\n",
    "articles = dataset.data[:100]\n",
    "print(len(articles))\n",
    "print(articles[1])\n",
    "print()\n",
    "print(\"<><><><>><><><>><><><><><>\")\n",
    "print()\n",
    "print(articles[2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We shall use the same familiar apporach of CountVectorizer to measure terms/words and their frequencies.  Our custom tokenization function for CountVectorizer is shown below. In this function, we are performing lemmatization on each word. In order to have correct lemma of a word, we also need to determine the part-of-speech tag of it. For example, the word saw as noun and as verb have different lemmas (root word) and of course they have different meanings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "done\n"
     ]
    }
   ],
   "source": [
    "from nltk.corpus import wordnet\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "\n",
    "#Custom function for toeknization\n",
    "def myTokenizer(text):\n",
    "    \n",
    "    lemmatizer = WordNetLemmatizer()\n",
    "    lemmas=[]\n",
    "    \n",
    "    for sent in nltk.sent_tokenize(text):\n",
    "        #nltk return the tag from Penntreebank tagsets\n",
    "        sentTag=nltk.pos_tag(nltk.word_tokenize(sent))\n",
    "        #print (sentTag)\n",
    "        for word, tag in sentTag:\n",
    "            # the problem wordnet lemmatizer is that, it recognizes only\n",
    "            # wordnet tags and not the PennTreebank tags. So we shall\n",
    "            # first convert Penntreebank tags to Wordnet tags\n",
    "            wordNetTag=getWordnetPos(tag)\n",
    "            if wordNetTag is None:\n",
    "                continue\n",
    "            else:\n",
    "                lemmas.append(lemmatizer.lemmatize(word,wordNetTag))\n",
    "                \n",
    "    return  lemmas\n",
    "    \n",
    "    \n",
    "# Function to convert \n",
    "#Penntreebank tags to wordnet tags\n",
    "def getWordnetPos(treebank_tag):\n",
    "\n",
    "    if treebank_tag.startswith('J'):\n",
    "        return wordnet.ADJ\n",
    "    elif treebank_tag.startswith('V'):\n",
    "        return wordnet.VERB\n",
    "    elif treebank_tag.startswith('N'):\n",
    "        return wordnet.NOUN\n",
    "    elif treebank_tag.startswith('R'):\n",
    "        return wordnet.ADV\n",
    "    else:#We are igonring everything else other than four of the above \n",
    "         # tags. You can add more if you like\n",
    "        return None      \n",
    "    \n",
    "print(\"done\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's add some stop words to our recipe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "done\n"
     ]
    }
   ],
   "source": [
    "import nltk\n",
    "import string\n",
    "stopWords=nltk.corpus.stopwords.words('english')\n",
    "stopWords+=[\"''\", \"'s\", \"...\", \"``\",\"--\",\"*\",\"-\"]\n",
    "stopWords+=list(string.punctuation)\n",
    "print(\"done\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Time to create a term document (or document term rather) matrix using the CountVectorizer class. All the parameters in this class are already dicussed in the earlier lab. If you need further help on parameters type help(CountVectorizer) in another cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100, 3903)\n",
      "Feature Names [\"'85\", \"'89\", \"'92\", \"'expected\", \"'fanatic\", \"'fecal\", \"'fragile\", \"'in-line\", \"'intifadah\", \"'m\", \"'new\", \"'news\", \"'noise\", \"'notify\", \"'re\", \"'scsi-1\", \"'spanish\", \"'to\", \"'ve\", '**', '**********************************************************************', '*.ini', '*boys', '*entirely*', '*finaly*', '*lack*', '*not*', '*nothing*', '*position*', '*really*', '*science*', '*specifically*', '*speed*', '+1', '+12', '-*', '-*-', '-0.5', '-1', '-12v', '-30-', '-324-9553', '-5v', '-617-3522', '-90', '-brando', '-ciao', '-eric', '-h', '-jason', '-rdd', '-sean', '-w', '..', '..deleted', '..robert', '..sam', '.238.', '.267', '.458', '.481', '.50', '.d', '.not', '.so', '.txt', '//', '//center', '/2', '/6', '/\\\\', '/\\\\|', '/contrib/xtrapv33_x11r5.tar.z', '/u.s', '/|/|', '/||', '/~', '0-5mb/s', '02-jul-92', '02194-2722', '1-20', '1-800-832-4778', '12-2', '12-5', '14-y-o', '1487-1489', '15-20mb/s', '16-bit/wide', '1625-1712', '1629-1695', '1808-1975', '1914-1920', '1918-1920', '1960s', '2-5', '2-5.', '2-door', '20,000-strong', '202-479-2258', '20th', '25-30k', '251-277', '268-9', '2nd', '3-1', '3-20', '3-3', '314-317', '315-316', '32-bit/wide', '336-9591', '349-380', '386-20mhz', '4-6mb/s', '41-160', '45-47', '4th', '5-6', '5-volt', '517-355-2178', '6-1', '6-2', '6-6', '6-7', '8-10', '8-12mb/s', '8-bit', '8-bit/scsi-1', '854-6889', ':openflags', '=====', '=======', '========================================================================', '========imagination', '\\\\\\\\', '\\\\_|\\\\/|_|/|', '^^^^^^^^^^^^^^^^^^^^^^', '_are_', '_depends_', '_entire_', '_hated_', '_rv', '_|\\\\\\\\_|', 'a000-c800', 'aardvark.ucs.uoknor.edu', 'ab', 'abandon', 'abdomen', 'abilene', 'able', 'abode', 'about-', 'abp', 'abp2.gif', 'abraham', 'absolute', 'abstract', 'abuse', 'academy', 'accel', 'acceleration', 'accept', 'acceptance', 'access', 'accessible', 'accessory', 'accident', 'accidentally', 'accommodation', 'accompany', 'accomplish', 'account', 'accuracy', 'accurate', 'achieve', 'acn', 'acne', 'acquire', 'acquisition', 'acquisition/control', 'acrv', 'act', 'action', 'active', 'activity', 'actually', 'actualy', 'acura', 'adam', 'adamantly', 'adana', 'adapter', 'adc', 'add', 'addition', 'additional', 'address', 'address/phone', 'adeos', 'adequately', 'adjoin', 'adjust', 'administrative', 'admit', 'admitted', 'admonition', 'adobe', 'adopt', 'adresses', 'adriaansen', 'advance', 'advanced', 'advantage', 'advice', 'advisory', 'affair', 'affect', 'affected', 'afford', 'age', 'agency', 'agent', 'ago', 'agree', 'ah', 'ai', 'aim', 'air', 'airbag', 'alameda', 'alamos', 'alarm', 'albert', 'alcohol', 'alert', 'alexis', 'alfa', 'algorithm', 'ali', 'align', 'alike', 'alive', 'allegedly', 'allen', 'allocate', 'allow', 'allstate', 'ally', 'almost', 'alone', 'along', 'alot', 'already', 'alright', 'also', 'alternative', 'always', 'am/fm', 'amaze', 'amazing', 'ambassador', 'ambient', 'ambition', 'american', 'amherst-clown', 'ami', 'ammunition', 'amount', 'amp', 'amplifier', 'amplify', 'analogy', 'analysis', 'analyze', 'anatolia', 'ancestor', 'ancient', 'angel', 'angle', 'angstrom', 'animal', 'anisetti', 'ankara', 'annihilate', 'annihilation', 'annoying', 'annul', 'answer', 'ansynchronous', 'antenna', 'anti', 'anticipation', 'anxiously', 'anybody', 'anymore', 'anyone', 'anything', 'anyway', 'anywhere', 'apart', 'aphelion', 'apocalypse', 'apollo', 'apostasy', 'apostate', 'apostle', 'app', 'appal', 'apparently', 'appeal', 'appear', 'appearance', 'appearence', 'appendix', 'apple', 'applicable', 'application', 'applies', 'apply', 'appoint', 'appreciate', 'appreciated', 'approach', 'appropriate', 'approval', 'approve', 'approximately', 'apr', 'april', 'aprs', 'apt', 'arab', 'arbitrary', 'architect', 'archive', 'archive-name', 'area', 'argic', 'argue', 'argument', 'ariane', 'arm', 'armenia', 'armenian', 'armenian-nazi', 'armistice', 'army', 'around', 'arounde', 'arrange', 'array', 'arrival', 'arrive', 'arrow', 'article', 'aryan', 'as-is', 'ascendent', 'aside', 'ask', 'aspect', 'aspiration', 'assault', 'assemble', 'assembler', 'assembly', 'assert', 'assessment', 'assign', 'assist', 'association', 'assume', 'assumption', 'assured', 'asteroid', 'astro-d', 'astrocytomas', 'astronomical', 'astronomy', 'astrophysical', 'asuka', 'atari', 'atf', 'atheism', 'atheist', 'athlete', 'athula', 'ati', 'atleast', 'atm', 'atmosphere', 'atmospheric', 'atrocity', 'attach', 'attack', 'attain', 'attempt', 'attention', 'attention-getting', 'attribution', 'au', 'audi', 'aug', 'august', 'auroral', 'aust', 'austin', 'australia', 'australian', 'author', 'authority', 'auto', 'autocad', 'autodoubler', 'autodoubler/diskdoubler', 'autoexec', 'automated', 'automatic', 'automatically', 'automation', 'automobile', 'available', 'avenue', 'average', 'aviv', 'avoid', 'await', 'aware', 'away', 'awd', 'awds', 'axaf', 'axis', 'ayoob', 'b', 'b/w', 'baalke', 'babi-i', 'back', 'back-stabbed', 'background', 'backside', 'backup', 'bad', 'badshmseg', 'bag', 'baikar', 'baker', 'balance', 'ban', 'band', 'bank', 'banner', 'bar', 'barbarous', 'bare', 'bargain', 'barrack', 'bartman', 'base', 'baseball', 'basically', 'basicly', 'basis', 'bat', 'batchfile', 'batf', 'battalion', 'batter', 'battery', 'batteryless', 'battlefield', 'bay', 'bayonet', 'bayur', 'bc', 'bc++', 'beachball', 'bearing', 'beautiful', 'beauty', 'become', 'bed', 'beemer', 'befall', 'beget', 'begin', 'beginning', 'behalf', 'belief', 'believe', 'belt', 'benefit', 'bent', 'berlin', 'best', 'bet', 'beta.lanl.gov', 'bevelizes', 'biased', 'bible', 'biblical', 'biennial', 'big', 'bike', 'bill', 'billing', 'binah.cc.brandeis.edu', 'binoculars', 'binolux', 'bios', 'birth', 'birthday', 'bit', 'bit-banging', 'bitlis', 'bizarre', 'bj-200', 'bk', 'black', 'blender', 'blind', 'blitz', 'bloch', 'block', 'blood', 'bloody', 'blow', 'blue', 'blue*', 'bmp', 'board', 'board-compressed', 'boast', 'bobby', 'body', 'bogus', 'boil', 'boiler', 'boiling-water', 'bolt', 'bomb', 'bonilla', 'book', 'boost', 'booster', 'boot', 'bosporus', 'boston', 'bother', 'bottom', 'boulder', 'boundary', 'box', 'boy', 'brag', 'brand', 'branham', 'branstad', 'brave', 'break', 'breifly', 'brewer', 'brewhas', 'brick', 'bricklin', 'bridge', 'brief', 'briefly', 'brilliant', 'bring', 'bristol', 'britain', 'british', 'bro', 'bronze/brown/orange', 'brother', 'brotherhood', 'brought', 'browser', 'btw-', 'budienny', 'buffer', 'bug', 'build', 'build-up', 'building', 'bullet', 'bullpen', 'bullshit', 'bumper', 'bunch', 'burdensome', 'bureau', 'burn', 'burnt', 'burst', 'burster', 'bury', 'bus', 'bus-1', 'butcher', 'butter-dishes', 'button', 'buttons/widgets', 'buy', 'buyer', 'buying', 'bva', 'c', \"c'mon\", 'c++', 'c/-', 'c5owcb.n3p', 'cabin', 'cable', 'cad.gatech.edu', 'cage', 'cain', 'calamity', 'caliber', 'calif.', 'california', 'california-irvine', 'call', 'callback', 'callison', 'callisto', 'cam', 'cambridge', 'camera', 'camera-ready', 'cameras', 'camp', 'campaign', 'campbell', 'camping', 'camshaft', \"can't\", 'canadian', 'canberra', 'cancel', 'canon', 'canopy', 'cap', 'capability', 'capacity', 'capitalized', 'captain', 'captaincy', 'capture', 'capture/convert', 'car', 'caravan', 'card', 'care', 'careful', 'carry', 'cart', 'cartridge', 'carve', 'case', 'cassette', 'cassini', 'caste', 'cataclysmic', 'catalog', 'catch', 'category', 'caucasian', 'caucasus', 'cause', 'cavalry', 'cc', 'ccd', 'cd', 'celbration', 'celica', 'cellular', 'census', 'center', 'centre', 'centuries-old', 'century', 'ceremonial', 'certain', 'certainly', 'cfile', 'chairman', 'chalk', 'challenge', 'change', 'character', 'characteristic', 'characterize', 'charge', 'charger', 'charles', 'chase', 'check', 'chemical', 'chemist', 'cheque', 'cherry', 'chevrolet', 'chicago', 'chief', 'child', 'chin', 'chip', 'choice', 'christ', 'christiaan', 'christian', 'christianity', 'church', 'cilicia', 'cimcoz', 'circuit', 'circulation', 'circumference', 'citizen', 'city', 'civil', 'civilian', 'civilization', 'claim', 'class', 'classification', 'clause', 'clean', 'clear', 'clearasil', 'clearest', 'clh', 'click', 'climb', 'clipper', 'clock', 'close', 'close-up', 'closed-cycle', 'closest', 'closing', 'closure', 'cloud', 'cloudy', 'clown', 'club', 'clutch', 'cmaeras', 'cmptrc.lonestar.org', 'co', 'co.', 'cock', 'cocker', 'cod', 'code', 'colaboration', 'cold', 'cold-blooded', 'collaboration', 'collect', 'collection', 'collector', 'college', 'collision', 'collude', 'colonel', 'colt', 'combat', 'combination', 'combine', 'combined', 'comdex', 'come', 'comet', 'command', 'commander', 'commandment', 'comment', 'commerce', 'commission', 'commit', 'committee', 'common', 'commonly', 'commonwealth', 'comms', 'communication', 'community', 'company', 'compel', 'competition', 'compiler', 'compiliers', 'complaint', 'complete', 'completely', 'completion', 'complexity', 'component', 'compound', 'comprehensive', 'compression', 'comprise', 'compute', 'computer', 'computing', 'computrac-richardson', 'comtemporary', 'conceal', 'conceivable', 'concentrate', 'concentration', 'concept', 'concern', 'concerted', 'conclude', 'conclusion', 'concur', 'condemnation', 'condense', 'condenser', 'condition', 'conduct', 'conference', 'conferencing', 'confident', 'config.syus', 'configuration', 'confirm', 'confrontation', 'congress', 'conjecture', 'conservative', 'consider', 'consideration', 'consist', 'consistently', 'conspiracy', 'constant', 'constantly', 'constituency', 'constitute', 'constrain', 'construction', 'consult', 'contact', 'contain', 'contaminate', 'contemporary', 'content', 'context', 'continue', 'continued', 'continuous', 'continuum', 'contribute', 'control', 'controler', 'controlled', 'controller', 'controversy', 'convenient', 'converse', 'convert', 'converter', 'convex', 'convincing', 'convoy', 'cookbook', 'cool', 'cooling', 'cooperation', 'coordination', 'coordinator', 'cop', 'copper', 'copy', 'cord', 'corner', 'corp', 'corps', 'corrado', 'correct', 'correction', 'correctly', 'corvette', 'cosmic', 'cost', 'council', 'count', 'counter', 'country', 'couple', 'course', 'court', 'covenant', 'cover', 'coverage', 'cow', 'cpu', 'crack', 'cradle', 'crankshaft', 'crash', 'crazy', 'create', 'creator', 'creature', 'credibility', 'crew', 'crime', 'criminal', 'crisis', 'criticize', 'crook', 'cross', 'crowd', 'crud', 'cruelty', 'cruise', 'crush', 'crust', 'crx', 'crypto', 'cryptography', 'cryptology', 'crystal', 'csiro', 'ctl-alt-del', 'ctrltest', 'culminate', 'cumulative', 'cure', 'current', 'currently', 'curve', 'customer', 'cut', 'cutler', 'cycle', 'cylinder', 'czar', 'czarist', \"d'artillerie\", \"d'erzeroum\", 'd-ga.', 'daily', 'dalacin', 'dalva', 'damage', 'damage/vandalism/theft', 'damn', 'dan', 'dandruff', 'dangerous', 'dans', 'dar-al-islam', 'dardanelles', 'darius', 'dark', 'darryl', 'dashboard', 'dashnags', 'dashnak', 'dashnakist', 'dashnaks', 'data', 'date', 'daughter', 'dave', 'david', 'day', 'day-to-day', 'dc', 'dd', 'de', 'de-ice', 'dead', 'deal', 'dealer', 'dealler', 'dean', 'death', 'debate', 'debra', 'debunk', 'dec', 'december', 'decendents', 'decent', 'decide', 'decision', 'deck', 'decker', 'declaration', 'declare', 'decompress', 'decut', 'deductible', 'deduction', 'deep', 'default', 'defeat', 'defence', 'defend', 'defense', 'defenseless', 'defensive', 'deficit', 'definately', 'define', 'definite', 'definitely', 'deflection', 'degradation', 'degree', 'delaunay', 'delaware', 'delay', 'delete', 'deletion', 'deliberately', 'deliver', 'delta', 'democracy', 'demographic', 'demonstrate', 'demonstration', 'demonstrator', 'dempster', 'deny', 'department', 'depend', 'depends', 'depletion', 'deploy', 'deport', 'depress', 'der', 'derivative', 'derive', 'derogatory', 'descend', 'describe', 'description', 'desert', 'deserving', 'design', 'designer', 'desirable', 'desk', 'destination', 'destroy', 'destruction', 'detail', 'detailed', 'deter', 'determine', 'deutsch-armenischen', 'deuxieme', 'devastation', 'develop', 'developer', 'development', 'devilish', 'dewey', 'diagnostic', 'dialog', 'dicta-93', 'dicta93', 'dictator', 'dictatorship', 'did1', 'die', 'diesel', 'diet', 'differ', 'difference', 'different', 'digital', 'dimension', 'dinner', 'dione', 'diplomatic', 'direct', 'direction', 'directive', 'directly', 'director', 'directory', 'dirt', 'dirtbike', 'dirty', 'disable', 'disarm', 'disarmament', 'discipline', 'disclaimer', 'discount', 'discover', 'discussion', 'disease', 'disfiguring', 'disheartened', 'disk', 'diskdoubler', 'disobey', 'display', 'dispute', 'disregardful', 'distance', 'distant', 'distinguish', 'distraction', 'ditto', 'diversity', 'division', 'dock', 'dockmaster', \"doctor's-prescription\", 'document', 'documentary', 'dod', \"dod'ers\", 'dodge', \"doens't\", 'dog', 'domain', 'domestic', 'dominique', 'donate', 'doom', 'door', 'dopey', 'doran', 'double', 'doubt', 'doug', 'download', 'downright', 'downshift', 'dr.', 'drain', 'dram', 'drastically', 'drawer', 'dream', 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'social', 'socialist', 'society', 'soderstrom', 'soft', 'soft-x-ray', 'software', 'solar', 'solar-studies', 'solder', 'soldier', 'solid', 'solution', 'somebody', 'someone', 'something', 'sometimes', 'somewhere', 'son', 'sony', 'sooner', 'sooo', 'sop', 'sorry', 'sort', 'soul', 'sound', 'source', 'south', 'southern', 'soviet', 'space', 'space/new_probes', 'spacecraft', 'spaced', 'spain', 'sparate', 'sparc2', 'speaker', 'speaking', 'spec', 'special', 'specialised', 'specially', 'specific', 'specifically', 'specification', 'spectrometer', 'spectrum', 'speculate', 'speed', 'spell', 'spend', 'spider', 'split', 'spontaneously', 'sport', 'spot', 'spotty', 'sppace', 'spray', 'spread', 'spring', 'spurious', 'squad', 'square', 'squid', 'sr-1000', 'srl-01', 'srt', 'ss10', 'ssf', 'sssssoooooooooooo', 'st', 'st.', 'stable', 'stac', 'stack', 'stadium', 'staff', 'stag', 'stalinism', 'stamp', 'stand', 'standard', 'standardize', 'stanford', 'star', 'start', 'starter', 'state', 'stated', 'statement', 'static', 'static/global', 'station', 'stats', 'status', 'statute', 'stay', 'stealth', 'steam', 'step', 'stephen', 'stereo', 'steve', 'stick', 'still', 'stinky', 'stole', 'stomache-contentsthan', 'stone', 'stop', 'store', 'story', 'straight', 'strange', 'stratospheric', 'stream', 'street', 'stress', 'strictly', 'strike', 'strip', 'strong', 'structure', 'strut', 'sts', 'sts-60', 'sts-68', 'stuck', 'student', 'study', 'stuff', 'stw3', 'style', 'styling', 'styrofoam', 'subaru', 'subcompact', 'subject', 'subjective', 'subjectiveness', 'submission', 'submit', 'subrogation', 'subscriber', 'subscription', 'subsequent', 'substance', 'substantial', 'substantially', 'subtly', 'succeed', 'success', 'successful', 'suchlike', 'suck', 'suero', 'suffer', 'sufficient', 'sufficiently', 'suggest', 'sultan', 'sumex-aim.stanford.edu', 'summarily', 'summarize', 'summary', 'summer', 'sun', 'sunbeam', 'sunday', 'sunni', 'super', 'superior', 'supervise', 'supply', 'support', 'suppose', 'suppression', 'supra', 'supras', 'supreme', 'suprised', 'sure', 'suresh', 'surface', 'surmali', 'surprisingly', 'surround', 'survey', 'survive', 'survivor', 'suspect', 'suspend', 'suspension', 'swap', 'swear', 'swing', 'switch', 'switch..', 'sydney', 'sympathy', 'synchronous', 'syntax', 'system', 'systematic', 'systematically', 'szold', 't-bird', 't.', 'table', 'tail', 'take', 'talk', 'talk.politics.guns', 'talk.religion', 'talmud', 'talon', 'tank', 'tant', 'tap', 'tape.tape', 'tapestry', 'target', 'tartar', 'taskilise', 'taunt', 'tax', 'tc', 'tcp/ip', 'teaching', 'team', 'tear', 'tech', 'technical', 'technician', 'technique', 'technology', 'ted', 'teenage', 'teenager', 'tel', 'telescope', 'television', 'tell', 'tellme', 'telnet', 'ten', 'tend', 'term', 'terminal', 'terminate', 'territorial', 'territory', 'terror', 'terrorism', 'terrorist', 'test', 'testing', 'texas', 'text', 'textual', 'texture', 'tga2pov', 'th', 'thank', 'thanks', 'thanx', \"that's\", 'thatnks', 'thats', 'theatre', 'theft', 'theives', \"there's\", 'therefore', 'theres', 'thermal', 'thermocouple', 'thesis', 'thicker', 'thing', 'think', 'thinking', 'third', 'thirteen', 'thomas', 'thon', 'though', 'thought', 'thousand', 'thread', 'threat', 'three-on-three', 'three-point', 'threefold', 'throttle', 'throw', 'thru', 'thrust', 'thumb', 'thumper', 'thunder', 'thunderbird', 'thursday', 'thus', 'thwaites', 'ticket', 'tie', 'tiff', 'tific', 'tiflis', 'till', 'tilt', 'tim', 'time', 'times-roman', 'timing', 'tiny', 'tirat', 'tire', 'tis-dom', 'tis.com', 'tists', 'titan', 'tobacco', 'today', 'tolerance', 'toll', 'tom', 'tombaker', 'ton', 'tony', 'tool', 'toolbox', 'top', 'topex/poseidon', 'topic', 'torah', 'toro', 'toronto', 'toshiba', 'total', 'total+100k', 'touch', 'tough', 'tour', 'towards', 'tower', 'towing', 'town', 'toyota', 'trace', 'track', 'trade', 'tradeoff', 'tradition', 'traditional', 'tragedy', 'train', 'training', 'traitorously', 'trajectory', 'tranquility', 'trans', 'transfer', 'transit', 'translab.its.uci.edu', 'translate', 'transport', 'transportation', 'trap', 'travel', 'traverse', 'treachery', 'treasonable', 'treat', 'treatment', 'treble', 'tree', 'trial', 'triangle', 'triangulate', 'tricky', 'trip', 'troop', 'trooper', 'trouble', 'troubled', 'troublemaker', 'truck', 'true', 'truly', 'trusted', 'truth', 'try', 'tsi', 'tsr', 'tuba', 'turbo', 'turk', 'turkey', 'turkish', 'turkiye', 'turn', 'twelve', 'twice', 'twillis', 'twin', 'two-year', 'tx', 'type', 'typically', 'tzvi', 'u', 'u.', 'u.s.', 'ugly', 'ultimate', 'ultra', 'ulysses', 'ulysses-', 'umbrella', 'umpire', 'unable', 'unashamedly', 'unconstitutional', 'uncultivated', 'under-secretary', 'under/into', 'underrated', 'understand', 'understanding', 'understood', 'underwriter', 'undoubtably', 'undoubtedly', 'unforgiven', 'unfortunately', 'unfurl', 'unh', 'union', 'unique', 'unit', 'united', 'univ', 'universe', 'university', 'unix', 'unknown', 'unlikely', 'unnecessarily', 'unnecessary', 'unpleasant', 'unregistered', 'unsafe', 'unscrupulous', 'unsynchronized', 'uokmax.ecn.uoknor.edu', 'upcoming', 'update', 'updated', 'updating', 'upgrade', 'upper', 'uprising', 'ups', 'upset', 'upshifts', 'upwards', 'urge', 'urgently', 'us', 'us/french', 'usa', 'usage', 'use', 'used', 'useless', 'user', 'user.exe', 'usual', 'usually', 'utility', 'utilize', 'utterance', 'v.', 'v6', 'vaccum', 'vaive', 'value', 'valve', 'van', 'vanbiesbrouck', 'vaporize', 'variable', 'variation', 'varied', 'variety', 'various', 'vast', 'vaughn', 'vega', 'vehicle', 'vehilce', 'vendor', 'venerable', 'venus', 'ver', 'verification', 'verified', 'version', 'versus', 'victory', 'video', 'view', 'vijay', 'vilayet', 'village', 'villiage', 'villiages', 'vincent', 'vincint', 'vintage', 'viola', 'violate', 'violation', 'violence', 'vip', 'virginia', 'virginia.edu', 'vision', 'visit', 'visualisation', 'visualization', 'vital', 'vitamin', 'voi-la', 'voic', 'voice', 'vol', 'vols.', 'voltage', 'volume', 'volunteer', 'volvos', 'vr6', 'vs', 'vukela', 'vulnerable', 'vvejga', 'vw', 'w', 'w-net', 'w.', 'w.e.d', 'w.va.', 'w/', 'w/2meg', 'wacko', 'wait', 'wake', 'walk', 'walker', 'wall', 'wallpaper', 'wanna-be', 'want', 'war', 'warning', 'warrant', 'warranty', 'wary', 'wash', 'washington', 'waste', 'watch', 'water', 'wave', 'way', 'wayne', 'weak', 'weak-encryption', 'weapon', 'wear', 'weave', 'week', 'weekend', 'wegman', 'weight', 'weitek', 'welcome', 'well', 'wes', 'west', 'western', 'wetware', 'weymouth', 'wfan', \"what's\", 'whatever', 'white', 'whois', 'whole', 'whooa', 'wide', 'wide-field', 'wide-wavelength', 'wierd', 'wife', 'william', 'willing', 'willis', 'willy', 'win', 'win.ini', 'wind', 'window', 'windows', 'windscreen', 'windshield', 'wing', 'wingless', 'winqwk', 'wipe', 'wiper', 'wire', 'wiretap', 'wiretapping', 'wish', 'withdraw', 'withdrawal', 'witnessed', 'wk', 'woman', \"won't-\", 'wonder', 'wonderful', 'wood', 'wool', 'word', 'work', 'works/has', 'workshop', 'workstation', 'world', 'world.std.com', 'worried', 'worry', 'worse', 'worth', 'worthless', 'worthwhile', 'would/could', 'wound', 'wow', 'wp', 'write', 'writer', 'writes', 'writing', 'wrong', 'wrongness', 'wwi', 'wwii', 'wyse', 'x', 'x-ray', 'x-soviet', 'x-terminal', 'x.', 'x/motif', 'x11r3', 'x11r4', 'x_shmputimage', 'xshmattach', 'xshmputimage', 'xtrap', 'xview', 'yamada', 'yankee', 'year', 'yearly', 'yehiam', 'yellow', 'yep', 'yerevan', 'yes', 'yesterday', 'yet', 'yhwh', 'yo', 'yoke', 'york', 'yoshiro', 'young', 'yr', 'yscvax.ysc.go.jp', 'zangezour', 'zangibasar', 'zealand', 'zeik-heil', 'zilkade', 'zone', 'zoologist', 'zoom', '|/', '|\\\\|', '||\\\\|\\\\', '~1500', '~24', '~|']\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "vect = CountVectorizer(max_features=10000, max_df=.70,\n",
    "                       tokenizer=myTokenizer, stop_words=stopWords)\n",
    "X = vect.fit_transform(articles)\n",
    "print (X.shape)\n",
    "print(\"Feature Names\",vect.get_feature_names())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we shall train LDA topic modeling algorithm on our data. In the code below, LDA have been asked to create only 5 topics (n_components) and told to iterate using EM algorithm up till 25 iterations. More details can be found by using help(LatentDirichletAllocation)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3903\n",
      "Documents and topics shape:  (100, 5)\n",
      "Topics and words shape: (5, 3903)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.decomposition import LatentDirichletAllocation\n",
    "\n",
    "#Initialize LDA\n",
    "vocabulary=X.shape[1] # total words in the training data\n",
    "print(vocabulary)\n",
    "topics=5 \n",
    "alpha=(1/topics) #alpha for LDA\n",
    "beta=(1/vocabulary)# beta for LDA\n",
    "\n",
    "#Note alpha and beta in actual LDA algorithm are actually vectors of decimal values and not a single decimal value\n",
    "# LDA implementation in Scikit does not take vectors as input for alpha and beta. So, we have to assign one value for \n",
    "# them. This means we can't really control the skewness of topics' dsitribution or skeweness of words's distrbution\n",
    "# and we just have to assign equal values to all in Scikit-learn. Another gensim library can help us solve this issue\n",
    "# (see Exercises)\n",
    "\n",
    "lda = LatentDirichletAllocation(n_components=topics, learning_method=\"batch\",\n",
    "max_iter=25, random_state=0, topic_word_prior=beta ,doc_topic_prior=alpha)\n",
    "\n",
    "\n",
    "# Train it.\n",
    "documentTopics = lda.fit_transform(X)\n",
    "\n",
    "print (\"Documents and topics shape: \", documentTopics.shape)\n",
    "print(\"Topics and words shape: {}\".format(lda.components_.shape))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's print five topics and top ten words in each topic. However, the last line of the code (topic.argsort[:-11:-1]) could be difficult to understand. Argsort gives the indexes of the values that sorts the data (words in topic) in ascending order. And the remaining part [-11:-1] sort them in descending order and picks the indexes of top 10 words. To understand this code play with the following commented code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Code to understand the following reverse sorting. \n",
    "#a=[1,2,3,4,5,6,7,8,9,10,11,12,13,14]\n",
    "# Try putting different negative and positive numbers and see what happens\n",
    "#a[:-4:-1]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Topic  0\n",
      "use,n't,get,run,problem,board,chip,scsi-1,say,window\n",
      "Topic  1\n",
      "-*-,year,car,insurance,go,rate,'m,n't,buy,think\n",
      "Topic  2\n",
      "use,know,n't,get,people,option,capability,way,well,many\n",
      "Topic  3\n",
      "armenian,russian,people,army,n't,genocide,reserve,ottoman,turkish,turk\n",
      "Topic  4\n",
      "probe,launch,mission,use,space,titan,orbiter,earth,orbit,n't\n"
     ]
    }
   ],
   "source": [
    "# Get the names of each word\n",
    "feature_names=vect.get_feature_names()\n",
    "topWords=-11 # 10 top words actually 11th is not printed\n",
    "# Go through the topic-word matrix\n",
    "for topicIdx, topic in enumerate(lda.components_):\n",
    "    print (\"Topic \",  topicIdx)\n",
    "    #Get top n words\n",
    "    print (\",\".join([feature_names[i]   for i in topic.argsort()[:topWords:-1]]))\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is some noise in our tokens but other than that some of the topics are quite distinct and mentioning different things. Let us also see what are the topic distributions of the five topics in first two documents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Topic 1 \t Topic 2  Topic 3\t Topic 4  Topic 5\n",
      "[0.00481578 0.98065237 0.00489119 0.00482679 0.00481387]\n",
      "\n",
      "[0.00385046 0.98470769 0.00383927 0.00379073 0.00381185]\n"
     ]
    }
   ],
   "source": [
    "print (\"Topic 1 \\t Topic 2  Topic 3\\t Topic 4  Topic 5\")\n",
    "print(documentTopics[0])\n",
    "print()\n",
    "print(documentTopics[1])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LSA \n",
    "\n",
    "LSA based topic modeling in scikit-learn is implemented in the same way as LDA but uses a TruncatedSVD class. Note that Scikit does not have PLSA implmeneted, so it is simply LSA (application of SVD on term-document matrix without proabilistic algorithm). Unfortunately other famous libraries in Python also do not implement PLSA."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Document topic shape (100, 5)\n",
      "Topics and word shape (5, 3903)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.decomposition import  TruncatedSVD\n",
    "lsa = TruncatedSVD(n_components=5)\n",
    "lsaDocTopic = lsa.fit_transform(X)\n",
    "print(\"Document topic shape\", lsaDocTopic.shape)\n",
    "print (\"Topics and word shape\", lsa.components_.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Topic 0:\n",
      "armenian,russian,people,army,genocide,ottoman,turkish,turk,muslim,war\n",
      "Topic 1:\n",
      "probe,launch,mission,titan,earth,space,orbiter,year,orbit,atmosphere\n",
      "Topic 2:\n",
      "year,car,insurance,rate,go,'m,high,get,========================================================================,pay\n",
      "Topic 3:\n",
      "-*-,**,mattress,suresh,-*,come,well,contact,pick,box\n",
      "Topic 4:\n",
      "option,power,ssf,use,capability,module,flight,redesign,station,team\n"
     ]
    }
   ],
   "source": [
    "for topic_idx, topic in enumerate(lsa.components_):\n",
    "    print (\"Topic %d:\" % (topic_idx))\n",
    "    print (\",\".join([feature_names[i]   for i in topic.argsort()[:-10-1:-1]]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modify the code to get rid of noise from the tokens. For example, there are lots of characters like *,/,-,=,\\,_. Feel free to remove any other noise that you deem appropripate.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "String Punctuation !\"#$%&'()*+,-./:;<=>?@[\\]^_`{|}~\n",
      "['and', 'but', 'd', 'em', 'how', 'i', 'it', 'm', 'off', 'oh', 're', 't', 'the', 'tis', 'twere', 've', 'we', 'well', 'why', 'yes', 'you', 'the', 'daily', 'had', 'he', 'her', 'him', 'his', 'i', 'me', 'not', 'our', 'she', 'that', 'the', 'very', 'we', 'you', 'ayear', 'ad', 'aaron', 'abandon', 'abase', 'abash', 'abate', 'abbey', 'abbeymill', 'abdon', 'abednego', 'abel', 'abhor', 'abiathar', 'abide', 'abideth', 'abigail', 'abihu', 'abijah', 'ability', 'abimelech', 'abinadab', 'abiram', 'abishai', 'abject', 'able', 'abner', 'aboard', 'abode', 'abolish', 'abominable', 'abomination', 'aboue', 'abound', 'abraham', 'abram', 'abroad', 'abrupt', 'abruptly', 'absalom', 'absence', 'absent', 'absentminded', 'absolute', 'absolutely', 'absorb', 'abstain', 'abstract', 'absurd', 'absurdity', 'abundance', 'abundant', 'abundantly', 'abuse', 'abyss', 'accelerate', 'accent', 'accept', 'acceptable', 'acceptance', 'access', 'accident', 'accidental', 'accidentally', 'accommodate', 'accommodation', 'accompany', 'accomplice', 'accomplish', 'accomplishment', 'accord', 'accordingly', 'accost', 'account', 'accumulate', 'accurately', 'accurse', 'accursed', 'accusation', 'accuse', 'accuser', 'accustom', 'achaia', 'achbor', 'ache', 'achieve', 'achish', 'acknowledge', 'acknowledgment', 'acorn', 'acquaint', 'acquaintance', 'acquainted', 'acquiescence', 'acquire', 'acquit', 'acre', 'act', 'acte', 'action', 'active', 'activity', 'actor', 'actress', 'actual', 'actually', 'actus', 'acute', 'adah', 'adaiah', 'adam', 'adamant', 'adamantine', 'adapt', 'adar', 'add', 'added', 'adder', 'addition', 'additional', 'address', 'adequately', 'adhere', 'adieu', 'adjoining', 'adjure', 'administer', 'admirable', 'admiral', 'admiration', 'admire', 'admirer', 'admit', 'admonish', 'adonijah', 'adopt', 'adoption', 'adoration', 'adore', 'adorn', 'adrift', 'adullam', 'adulterer', 'adulteress', 'adulterous', 'adultery', 'advance', 'advanced', 'advancing', 'advantage', 'adventure', 'adventurous', 'adversary', 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'unconscious', 'unconsciously', 'uncouth', 'uncover', 'unctuous', 'undaunted', 'undefiled', 'undergo', 'underground', 'underneath', 'understand', 'understandeth', 'understanding', 'understood', 'undertake', 'undertone', 'undo', 'undone', 'undoubtedly', 'undress', 'undulate', 'unearthly', 'uneasiness', 'uneasy', 'unequal', 'unexceptionable', 'unexpected', 'unfair', 'unfathomable', 'unfeeling', 'unfeigned', 'unfit', 'unfold', 'unfortunate', 'unfortunately', 'ungodly', 'ungracious', 'ungrateful', 'unhappiness', 'unhappy', 'unheard', 'unholy', 'unicorn', 'uniform', 'unimaginable', 'union', 'unique', 'unite', 'united', 'universal', 'universally', 'universe', 'unjust', 'unkind', 'unkindness', 'unknown', 'unleavened', 'unlikely', 'unlock', 'unluckily', 'unlucky', 'unmarried', 'unmeaning', 'unmistakable', 'unmoved', 'unnamed', 'unnatural', 'unnecessary', 'unobserved', 'unobtrusive', 'unpardonable', 'unpleasant', 'unprecedented', 'unprofitable', 'unpunished', 'unquestionable', 'unquestionably', 'unreasonable', 'unreasonably', 'unreserve', 'unrighteous', 'unrighteousness', 'unseen', 'unspeakable', 'unsuccessful', 'unsuspected', 'untie', 'untimely', 'unto', 'untold', 'untouched', 'untried', 'unusual', 'unusually', 'unwelcome', 'unwell', 'unwholesome', 'unwilling', 'unwillingness', 'unwise', 'unwittingly', 'unworthy', 'uphold', 'upland', 'uplift', 'uplifted', 'upon', 'upper', 'uppercross', 'uppermost', 'upright', 'uprightly', 'uprightness', 'uproar', 'upset', 'upside', 'upstairs', 'upward', 'upwards', 'ur', 'urge', 'urgent', 'uri', 'uriah', 'uriel', 'urijah', 'urim', 'usage', 'use', 'useful', 'usefulness', 'useless', 'useth', 'usher', 'usual', 'usually', 'usury', 'utility', 'utmost', 'utter', 'utterance', 'uttered', 'uttereth', 'utterly', 'uttermost', 'uz', 'uzza', 'uzzi', 'uzziah', 'uzziel', 'v', 'vacancy', 'vacant', 'vagabond', 'vague', 'vaguely', 'vail', 'vain', 'vainly', 'vale', 'valiant', 'valiantly', 'valley', 'valognes', 'valour', 'valuable', 'value', 'van', 'vane', 'vanish', 'vanity', 'vanquish', 'vapor', 'vapour', 'variation', 'varied', 'variety', 'various', 'vary', 'vashti', 'vast', 'vastly', 'vault', 'vaunt', 'vegetable', 'vehemence', 'vehement', 'vehemently', 'vehicle', 'veil', 'vein', 'velocity', 'velvet', 'venerable', 'venetian', 'vengeance', 'vengeful', 'venison', 'venom', 'vent', 'venture', 'verbal', 'verdant', 'verdure', 'verge', 'verie', 'verily', 'verse', 'version', 'vertebra', 'vertue', 'vessel', 'vesture', 'vesuvius', 'veteran', 'vex', 'vexation', 'vexed', 'vi', 'vial', 'vibrate', 'vibration', 'vicarage', 'vice', 'vicinity', 'vicissitude', 'victim', 'victor', 'victoria', 'victorious', 'victory', 'victual', 'view', 'vigil', 'vigilance', 'vigilant', 'vigorous', 'vigorously', 'vigour', 'vii', 'viii', 'vile', 'villa', 'village', 'villager', 'villain', 'villaine', 'villars', 'vine', 'vinegar', 'vineyard', 'vintage', 'violence', 'violent', 'violently', 'violet', 'violetta', 'viper', 'virgin', 'virginia', 'virginity', 'virtue', 'virtuous', 'visage', 'vishnoo', 'visible', 'visibly', 'vision', 'visit', 'visitation', 'visitor', 'vista', 'vital', 'vitality', 'vivacity', 'vivid', 'vnder', 'vnderstand', 'vnnaturall', 'vnto', 'vocation', 'voice', 'voiceless', 'void', 'volcano', 'volition', 'volley', 'volume', 'volumnius', 'voluntarily', 'voluntary', 'vomit', 'vote', 'vouchsafe', 'vouchsafed', 'vow', 'vowes', 'voyage', 'voyager', 'voyce', 'vp', 'vpon', 'vs', 'vse', 'vulgar', 'vulgarity', 'vulture', 'w', 'wad', 'wade', 'wafer', 'waft', 'wag', 'wage', 'wager', 'wagon', 'waif', 'wail', 'wailing', 'waist', 'waistcoat', 'wait', 'waiter', 'waiteth', 'wake', 'wakeful', 'waken', 'waking', 'wale', 'walk', 'walke', 'walkest', 'walketh', 'walking', 'walkingstick', 'wall', 'wallis', 'wallow', 'walrus', 'walter', 'waltz', 'wan', 'wand', 'wander', 'wanderer', 'wandereth', 'wandering', 'want', 'wanteth', 'wanting', 'wanton', 'war', 'warble', 'ward', 'warder', 'wardrobe', 'ware', 'warfare', 'warlike', 'warm', 'warmly', 'warmth', 'warn', 'warning', 'warp', 'warrant', 'warre', 'warrior', 'wary', 'wast', 'wash', 'washing', 'washington', 'wast', 'waste', 'wasteful', 'watch', 'watchful', 'watching', 'watchman', 'water', 'wateringplace', 'watery', 'wave', 'waver', 'waving', 'wax', 'waxed', 'waxen', 'way', 'wayes', 'wayside', 'weak', 'weake', 'weaken', 'weakly', 'weakness', 'wealth', 'wealthy', 'wean', 'weapon', 'wear', 'weare', 'wearily', 'weariness', 'weary', 'weather', 'weave', 'weaver', 'web', 'wed', 'wedding', 'wedge', 'wednesday', 'wee', 'weel', 'weed', 'week', 'weep', 'weepe', 'weeping', 'weigh', 'weight', 'weighty', 'welcome', 'weld', 'welfare', 'well', 'wellbred', 'wellinformed', 'wellknown', 'welllooking', 'wellmeaning', 'wend', 'wentest', 'wentworth', 'wept', 'wert', 'west', 'western', 'westgate', 'weston', 'westons', 'westward', 'wet', 'weymouth', 'whale', 'whales', 'whaleboat', 'whaleline', 'whaleship', 'whaleships', 'whalebone', 'whaleman', 'whalemen', 'whaler', 'whaling', 'wharf', 'whatever', 'whatsoever', 'wheat', 'wheedle', 'wheel', 'wheeler', 'whelm', 'whelp', 'whence', 'whenever', 'whereas', 'whereat', 'whereby', 'wherefore', 'wherein', 'whereof', 'whereon', 'wheresoever', 'whereto', 'whereunto', 'whereupon', 'wherever', 'wherewith', 'whet', 'whiff', 'whilst', 'whim', 'whip', 'whirl', 'whirlpool', 'whirlwind', 'whisker', 'whisky', 'whisper', 'whispering', 'whistle', 'whit', 'white', 'whiteness', 'whither', 'whithersoever', 'whiting', 'whitman', 'whoever', 'whole', 'wholesome', 'wholly', 'wholsome', 'whomsoever', 'whore', 'whoredom', 'whoring', 'whoso', 'whosoever', 'wicked', 'wickedly', 'wickedness', 'wid', 'wide', 'widely', 'wider', 'widow', 'wield', 'wife', 'wig', 'wight', 'wigwam', 'wild', 'wilderness', 'wildly', 'wildness', 'wile', 'wilful', 'wilkinson', 'william', 'williams', 'willing', 'willingly', 'willoughby', 'willow', 'wilson', 'wilt', 'win', 'wind', 'winde', 'winding', 'windlass', 'window', 'windows', 'windsor', 'windward', 'windy', 'wine', 'winepress', 'wing', 'winged', 'wingfield', 'wink', 'winter', 'winthrop', 'wintry', 'wipe', 'wire', 'wisdom', 'wise', 'wisedome', 'wisely', 'wiser', 'wish', 'wist', 'wit', 'witch', 'witchcraft', 'withal', 'withall', 'withdraw', 'withdrawn', 'wither', 'withereth', 'witherspoon', 'withhold', 'withholden', 'withstand', 'witness', 'witty', 'wizard', 'woe', 'wolf', 'woman', 'womanhood', 'womb', 'wonder', 'wonderful', 'wonderfully', 'wondering', 'wonderous', 'wondrous', 'wont', 'wonted', 'wood', 'wooded', 'wooden', 'woodhouse', 'woodland', 'woodpecker', 'woof', 'wool', 'woollen', 'word', 'work', 'worke', 'worker', 'worketh', 'working', 'workman', 'workmanship', 'workshop', 'world', 'worldly', 'worm', 'wormwood', 'worn', 'worry', 'worse', 'worship', 'worshipper', 'worst', 'worth', 'worthless', 'worthy', 'wot', 'wouldst', 'wouldest', 'wouldst', 'wound', 'wounded', 'woven', 'wrap', 'wrapt', 'wrath', 'wreath', 'wreathen', 'wreck', 'wrecked', 'wrench', 'wrest', 'wrestle', 'wretch', 'wretched', 'wretchedness', 'wriggle', 'wring', 'wrinkle', 'wrinkled', 'wrist', 'writ', 'write', 'writer', 'writhe', 'writing', 'wrong', 'wrongfully', 'wroth', 'wrought', 'xi', 'xii', 'xiii', 'xv', 'xvi', 'xviii', 'yacht', 'yankee', 'yard', 'yarman', 'yarn', 'yawn', 'ye', 'yea', 'year', 'yearly', 'yearn', 'yearning', 'yeeld', 'yell', 'yellow', 'yes', 'yesterday', 'yet', 'yield', 'yojo', 'yoke', 'yon', 'yonder', 'yong', 'york', 'yorkshire', 'you', 'young', 'youth', 'youthful', 'zabad', 'zaccur', 'zacharias', 'zadok', 'zalmunna', 'zeal', 'zealand', 'zealous', 'zebadiah', 'zebah', 'zebedee', 'zebulun', 'zechariah', 'zedekiah', 'zelia', 'zelophehad', 'zephaniah', 'zerah', 'zerubbabel', 'zeruiah', 'ziba', 'zibeon', 'zichri', 'zidon', 'zidonians', 'ziklag', 'zilpah', 'zimri', 'zin', 'zion', 'ziph', 'zippor', 'zoan', 'zoar', 'zobah', 'zone', 'zorah']\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10000, 9567)\n",
      "Documents and topics shape:  (10000, 5)\n",
      "Topics and words shape: (5, 9567)\n",
      "Topic  0\n",
      "astonish,ill,turn,bad,whale,lye,wave,cry,sleep,swell\n",
      "Topic  1\n",
      "curse,toss,rise,fast,shake,flutter,break,canst,hiss,marry\n",
      "Topic  2\n",
      "learn,shock,know,excite,eat,startle,return,yes,shudder,settle\n",
      "Topic  3\n",
      "carve,cover,well,need,drip,whisper,wouldst,print,muffle,grow\n",
      "Topic  4\n",
      "mr,anoint,point,flame,rustle,appear,proceed,fix,bury,fancy\n",
      "Topic 1 \t Topic 2  Topic 3\t Topic 4  Topic 5\n",
      "[0.2 0.2 0.2 0.2 0.2]\n",
      "\n",
      "[0.2 0.2 0.2 0.2 0.2]\n"
     ]
    }
   ],
   "source": [
    "print(\"String Punctuation\",string.punctuation)\n",
    "\n",
    "#We can use the function maketrans() to create a mapping table. We can create an empty mapping table, \n",
    "#but the third argument of this function allows us to list all \n",
    "#of the characters to remove during the translation process. For example:\n",
    "#translate() method takes the translation table to replace/translate characters in the given string as per the mapping table\n",
    "\n",
    "punc_map = str.maketrans('', '', string.punctuation)\n",
    "final_words = [word.translate(punc_map) for word in vect.get_feature_names()]\n",
    "print(final_words)\n",
    "\n",
    "\n",
    "X = vect.fit_transform(final_words)\n",
    "print (X.shape)\n",
    "#print(vect.get_feature_names())\n",
    "\n",
    "\n",
    "# Train it\n",
    "documentTopics = lda.fit_transform(X)\n",
    "\n",
    "print(\"Documents and topics shape: \", documentTopics.shape)\n",
    "print(\"Topics and words shape: {}\".format(lda.components_.shape))\n",
    "\n",
    "####### Test\n",
    "# Get the names of each word\n",
    "feature_names=vect.get_feature_names()\n",
    "topWords=-11 # 10 top words actually 11th is not printed\n",
    "# Go through the topic-word matrix\n",
    "for topicIdx, topic in enumerate(lda.components_):\n",
    "    print (\"Topic \",  topicIdx)\n",
    "    #Get top n words\n",
    "    print (\",\".join([feature_names[i]   for i in topic.argsort()[:topWords:-1]]))\n",
    "    \n",
    "print (\"Topic 1 \\t Topic 2  Topic 3\\t Topic 4  Topic 5\")\n",
    "print(documentTopics[0])\n",
    "print()\n",
    "print(documentTopics[1])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Download some documents (minimum 10 dcs) from Gutenberg project: https://www.gutenberg.org/. Apply both LDA and PLSA on the documents to find out different topics discussed in the documents.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['austen-emma.txt', 'austen-persuasion.txt', 'austen-sense.txt', 'bible-kjv.txt', 'blake-poems.txt', 'bryant-stories.txt', 'burgess-busterbrown.txt', 'carroll-alice.txt', 'chesterton-ball.txt', 'chesterton-brown.txt', 'chesterton-thursday.txt', 'edgeworth-parents.txt', 'melville-moby_dick.txt', 'milton-paradise.txt', 'shakespeare-caesar.txt', 'shakespeare-hamlet.txt', 'shakespeare-macbeth.txt', 'whitman-leaves.txt']\n",
      "<class 'list'>\n",
      "(1, 10000)\n",
      "Documents and topics shape:  (1, 5)\n",
      "Topics and words shape: (5, 10000)\n",
      "Topic 1 \t Topic 2  Topic 3\t Topic 4  Topic 5\n",
      "[9.99999096e-01 2.26085266e-07 2.26085266e-07 2.26085266e-07\n",
      " 2.26085266e-07]\n",
      "Document topic shape (1, 1)\n",
      "Topics and word shape (1, 10000)\n",
      "Topic 0:\n",
      "say,lord,come,go,thou,god,thy,man,make,thee\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Software_install\\Python3.6\\lib\\site-packages\\sklearn\\decomposition\\truncated_svd.py:192: RuntimeWarning: invalid value encountered in true_divide\n",
      "  self.explained_variance_ratio_ = exp_var / full_var\n"
     ]
    }
   ],
   "source": [
    "from nltk.corpus import gutenberg\n",
    "import nltk\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.decomposition import LatentDirichletAllocation\n",
    "from nltk.corpus import wordnet\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "import string\n",
    "\n",
    "stopWords=nltk.corpus.stopwords.words('english')\n",
    "stopWords+=[\"''\", \"'s\", \"...\", \"``\",\"--\",\"*\",\"-\"]\n",
    "stopWords+=list(string.punctuation)\n",
    "#print(\"done\")\n",
    "\n",
    "#Custom function for toeknization\n",
    "def myTokenizer(text):\n",
    "    \n",
    "    lemmatizer = WordNetLemmatizer()\n",
    "    lemmas=[]\n",
    "    \n",
    "    for sent in nltk.sent_tokenize(text):\n",
    "        #nltk return the tag from Penntreebank tagsets\n",
    "        sentTag=nltk.pos_tag(nltk.word_tokenize(sent))\n",
    "        #print (sentTag)\n",
    "        for word, tag in sentTag:\n",
    "            # the problem wordnet lemmatizer is that, it recognizes only\n",
    "            # wordnet tags and not the PennTreebank tags. So we shall\n",
    "            # first convert Penntreebank tags to Wordnet tags\n",
    "            wordNetTag=getWordnetPos(tag)\n",
    "            if wordNetTag is None:\n",
    "                continue\n",
    "            else:\n",
    "                lemmas.append(lemmatizer.lemmatize(word,wordNetTag))\n",
    "                \n",
    "    return  lemmas\n",
    "    \n",
    "    \n",
    "# Function to convert \n",
    "#Penntreebank tags to wordnet tags\n",
    "def getWordnetPos(treebank_tag):\n",
    "\n",
    "    if treebank_tag.startswith('J'):\n",
    "        return wordnet.ADJ\n",
    "    elif treebank_tag.startswith('V'):\n",
    "        return wordnet.VERB\n",
    "    elif treebank_tag.startswith('N'):\n",
    "        return wordnet.NOUN\n",
    "    elif treebank_tag.startswith('R'):\n",
    "        return wordnet.ADV\n",
    "    else:#We are igonring everything else other than four of the above \n",
    "         # tags. You can add more if you like\n",
    "        return None      \n",
    "    \n",
    "#print(\"done\")\n",
    "\n",
    "print(gutenberg.fileids())\n",
    "\n",
    "fileids= gutenberg.fileids()\n",
    "print(type(fileids))\n",
    "\n",
    "articles = nltk.corpus.gutenberg.raw(fileids)\n",
    "\n",
    "vect = CountVectorizer(max_features=10000,\n",
    "                       tokenizer=myTokenizer, stop_words=stopWords)\n",
    "\n",
    "\n",
    "X = vect.fit_transform([articles])\n",
    "print (X.shape)\n",
    "#print(vect.get_feature_names())\n",
    "\n",
    "#Initialize LDA\n",
    "vocabulary=X.shape[1] # total words in the training data\n",
    "topics=5 \n",
    "alpha=(1/topics) #alpha for LDA\n",
    "beta=(1/vocabulary)# beta for LDA\n",
    "\n",
    "#Note alpha and beta in actual LDA algorithm are actually vectors of decimal values and not a single decimal value\n",
    "# LDA implementation in Scikit does not take vectors as input for alpha and beta. So, we have to assign one value for \n",
    "# them. This means we can't really control the skewness of topics' dsitribution or skeweness of words's distrbution\n",
    "# and we just have to assign equal values to all in Scikit-learn. Another gensim library can help us solve this issue\n",
    "# (see Exercises)\n",
    "\n",
    "lda = LatentDirichletAllocation(n_components=topics, learning_method=\"batch\",\n",
    "max_iter=25, random_state=0, topic_word_prior=beta ,doc_topic_prior=alpha)\n",
    "\n",
    "\n",
    "# Train it.\n",
    "documentTopics = lda.fit_transform(X)\n",
    "\n",
    "print (\"Documents and topics shape: \", documentTopics.shape)\n",
    "print(\"Topics and words shape: {}\".format(lda.components_.shape))\n",
    "\n",
    "# Get the names of each word\n",
    "feature_names=vect.get_feature_names()\n",
    "topWords=-11 # 10 top words actually 11th is not printed\n",
    "# Go through the topic-word matrix\n",
    "#for topicIdx, topic in enumerate(lda.components_):\n",
    "#    print (\"Topic \",  topicIdx)\n",
    "    #Get top n words\n",
    "#    print (\",\".join([feature_names[i]   for i in topic.argsort()[:topWords:-1]]))\n",
    "    \n",
    "print (\"Topic 1 \\t Topic 2  Topic 3\\t Topic 4  Topic 5\")\n",
    "print(documentTopics[0])\n",
    "#print()\n",
    "#print(documentTopics[1])\n",
    "\n",
    "#### LSA\n",
    "from sklearn.decomposition import  TruncatedSVD\n",
    "lsa = TruncatedSVD(n_components=5)\n",
    "lsaDocTopic = lsa.fit_transform(X)\n",
    "print(\"Document topic shape\", lsaDocTopic.shape)\n",
    "print (\"Topics and word shape\", lsa.components_.shape)\n",
    "\n",
    "for topic_idx, topic in enumerate(lsa.components_):\n",
    "    print (\"Topic %d:\" % (topic_idx))\n",
    "    print (\",\".join([feature_names[i]   for i in topic.argsort()[:-10-1:-1]]))\n",
    "    "
   ]
  },
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