TF-IDF_Vectorizing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Vectorizing Raw Data: TF-IDF"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TF-IDF\n",
    "\n",
    "Creates a document-term matrix where the columns represent single unique terms (unigrams) but the cell represents a weighting meant to represent how important a word is to a document.\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read in text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import re\n",
    "import string\n",
    "import nltk\n",
    "pd.set_option('display.max_colwidth', 100)\n",
    "\n",
    "stopwords = nltk.corpus.stopwords.words('english')\n",
    "ps = nltk.PorterStemmer()\n",
    "\n",
    "data = pd.read_csv(\"SMSSpamCollection.tsv\", sep='\\t')\n",
    "data.columns = ['label', 'body_text']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create function to remove punctuation, tokenize, remove stopwords, and stem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def clean_text(text):\n",
    "    text = \"\".join([word.lower() for word in text if word not in string.punctuation])\n",
    "    tokens = re.split('\\W+', text)\n",
    "    text = [ps.stem(word) for word in tokens if word not in stopwords]\n",
    "    return text"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Apply TfidfVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5567, 8104)\n",
      "['', '0', '008704050406', '0089mi', '0121', '01223585236', '01223585334', '0125698789', '02', '020603', '0207', '02070836089', '02072069400', '02073162414', '02085076972', '020903', '021', '050703', '0578', '06', '060505', '061104', '07008009200', '07046744435', '07090201529', '07090298926', '07099833605', '071104', '07123456789', '0721072', '07732584351', '07734396839', '07742676969', '07753741225', '0776xxxxxxx', '07786200117', '077xxx', '078', '07801543489', '07808', '07808247860', '07808726822', '07815296484', '07821230901', '0784987', '0789xxxxxxx', '0794674629107880867867', '0796xxxxxx', '07973788240', '07xxxxxxxxx', '0800', '08000407165', '08000776320', '08000839402', '08000930705', '08000938767', '08001950382', '08002888812', '08002986030', '08002986906', '08002988890', '08006344447', '0808', '08081263000', '08081560665', '0825', '0844', '08448350055', '08448714184', '0845', '08450542832', '08452810071', '08452810073', '08452810075over18', '0870', '08700621170150p', 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'09066382422', '09066612661', '09066649731from', '09066660100', '09071512432', '09071512433', '09071517866', '09077818151', '09090204448', '09090900040', '09094100151', '09094646631', '09094646899', '09095350301', '09096102316', '09099725823', '09099726395', '09099726429', '09099726481', '09099726553', '09111030116', '09111032124', '09701213186', '0anetwork', '1', '10', '100', '1000', '10000', '100000', '1000call', '100603', '100psm', '1010', '1013', '101mega', '1030', '10803', '10am', '10am7pm', '10am9pm', '10k', '10p', '10pmin', '10ppm', '10th', '11', '1120', '113', '1131', '11414', '1146', '1148', '116', '1172', '118pmsg', '11mth', '12', '120', '12000pe', '1205', '121', '1225', '123', '1230', '125', '1250', '125gift', '128', '12hour', '12hr', '12mth', '12price', '13', '130', '131004', '1327', '13404', '139', '140', '1405', '140ppm', '145', '1450', '146tf150p', '14thmarch', '150', '1500', '150ea', '150morefrmmob', '150msg', '150mtmsgrcvd18', '150p', '150pday', '150perweeksub', '150perwksub', '150pm', '150pmeg', '150pmin', '150pmmorefrommobile2bremovedmobypobox734ls27yf', '150pmsg', '150pmsgrcvd', '150pmsgrcvdhgsuite3422landsroww1j6hl', '150pmt', '150pmtmsg', '150pmtmsgrcvd18', '150ppermesssubscript', '150ppm', '150ppmpobox10183bhamb64x', '150ppmsg', '150prcvd', '150psm', '150ptext', '150ptone', '150pw', '150pwk', '150rcvd', '150week', '150wk', '151', '1526', '153', '15541', '15pmin', '16', '1680', '169', '16onli', '177', '18', '180', '181104', '1843', '186', '18onli', '18ptxt', '18yr', '195', '1956669', '1appledayno', '1childish', '1cup', '1da', '1er', '1hanuman', '1hi', '1hr', '1im', '1lemondayno', '1mcflyall', '1million', '1minmobsmor', '1minmobsmorelkpobox177hp51fl', '1minmoremobsemspobox45po139wa', '1month', '1pm', '1s', '1st', '1st4term', '1stchoicecouk', '1stone', '1tulsi', '1u', '1unbreak', '1winaweek', '1winawk', '1x150pwk', '1yf', '2', '20', '200', '2000', '20000', '2003', '2004', '2005', '2006', '2007', '2025050', '20f', '20m12aq', '20p', '20pmin', '21', '211104', '215', '21870000hi', '21m', '21st', '22', '220cm2', '23', '2309', '230ish', '24', '241', '241004', '247mp', '24hr', '24m', '24th', '25', '250', '250k', '255', '25f', '25p', '260305', '261004', '261104', '2667', '26th', '2703', '27603', '28', '2814032', '285', '28day', '28th', '28thfebtc', '290305', '29100', '29m', '2b', '2bajarangabali', '2bold', '2c', '2channel', '2day', '2daylov', '2docdpleas', '2end', '2exit', '2ez', '2getha', '2geva', '2go', '2godid', '2gthr', '2hook', '2hr', '2i', '2im', '2kbsubject', '2marrow', '2moro', '2morow', '2morro', '2morrow', '2morrowxxxx', '2mro', '2mrw', '2mwen', '2naughti', '2nd', '2nhite', '2night', '2nite', '2nitetel', '2optout', '2optoutd3wv', '2p', '2polic', '2px', '2rcv', '2stop', '2stoptx', '2stoptxt', '2u', '2u2', '2untam', '2watershd', '2waxsto', '2when', '2wk', '2wt', '2wu', '2year', '2yr', '3', '30', '300', '3000', '300603', '300603tcsbcm4235wc1n3xxcallcost150ppmmobilesvari', '300p', '3030', '30apr', '30pptxt', '30th', '31', 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'4press', '4rowdi', '4some1', '4tctxt', '4th', '4the', '4thnovbehind', '4txt120p', '4txtú120', '4u', '4ui', '4utxt', '4w', '4ward', '4wrd', '4year', '5', '50', '500', '5000', '500000', '505060', '50award', '50p', '515', '515pm', '5226', '5249', '526', '528', '530', '532', '54', '542', '545', '5903', '5digit', '5free', '5ful', '5garden', '5gentli', '5i', '5ish', '5k', '5min', '5ml', '5month', '5p', '5pm', '5sankatmochan', '5terror', '5th', '5wb', '5we', '5wkg', '5wq', '5year', '6', '600', '6031', '60400thousadi', '60p', '60pmin', '61200', '61610', '62220cncl', '6230', '62468', '62735', '630', '63mile', '645', '645pm', '650', '6669', '67441233', '68866', '69101', '69200', '69669', '69696', '69698', '69855', '6986618', '69876', '69888', '69888nyt', '69911', '69969', '69988', '6cruel', '6day', '6hl', '6housemaid', '6hr', '6ish', '6miss', '6month', '6pm', '6ramaduth', '6romant', '6th', '6time', '6wu', '6zf', '7', '700', '71', '725', '7250', '7250i', '730', '730ish', '730pm', '731', '74355', 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'action', 'activ', 'activ8', 'actor', 'actual', 'acwicmb3cktz8r74', 'ad', 'adam', 'add', 'addamsfa', 'addi', 'addict', 'address', 'addressul', 'adewal', 'adi', 'adjust', 'admin', 'administr', 'admir', 'admiss', 'admit', 'admiti', 'ador', 'adp', 'adress', 'adrian', 'adrink', 'adsens', 'adult', 'advanc', 'adventur', 'advic', 'advis', 'advisor', 'aeronaut', 'aeroplan', 'afew', 'affair', 'affect', 'affection', 'affectionsamp', 'affidavit', 'afford', 'afghanistan', 'afraid', 'africa', 'african', 'aft', 'afternon', 'afternoon', 'afterward', 'aftr', 'ag', 'againcal', 'againlov', 'agalla', 'age', 'age16', 'age16150ppermesssubscript', 'age23', 'agenc', 'agent', 'agesr', 'agidhan', 'ago', 'agocusoon', 'agre', 'agreen', 'ah', 'aha', 'ahead', 'ahge', 'ahhh', 'ahhhhjust', 'ahmad', 'ahnow', 'ahold', 'ahsen', 'ahth', 'ahwhat', 'aid', 'aig', 'aight', 'aint', 'air', 'air1', 'airport', 'airtel', 'aiya', 'aiyah', 'aiyar', 'aiyo', 'ajith', 'ak', 'aka', 'akonlon', 'al', 'alaikkumprid', 'alaipayuth', 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'steve', 'stevelik', 'stewarts', 'steyn', 'sth', 'sthi', 'stick', 'sticki', 'stifl', 'stil', 'still', 'stillmayb', 'stink', 'stitch', 'stock', 'stockport', 'stolen', 'stomach', 'stomp', 'stone', 'stoner', 'stool', 'stop', 'stop2', 'stop2stop', 'stopbcm', 'stopc', 'stopcost', 'stoptxt', 'stoptxtstop', 'store', 'storelik', 'stori', 'storm', 'str', 'str8', 'straight', 'strain', 'strang', 'stranger', 'strangersaw', 'stream', 'street', 'streetshal', 'stress', 'stressful', 'stretch', 'strewn', 'strict', 'strike', 'string', 'strip', 'stripe', 'stroke', 'strong', 'strongbuy', 'strongli', 'strt', 'strtd', 'struggl', 'stu', 'stubborn', 'stuck', 'studdi', 'student', 'studentfinanci', 'studentsthi', 'studi', 'studio', 'studyn', 'stuf', 'stuff', 'stuff42moro', 'stuffleav', 'stuffwhi', 'stun', 'stupid', 'stupidit', 'style', 'stylish', 'stylist', 'sub', 'subject', 'sublet', 'submit', 'subpoli', 'subscrib', 'subscribe6gbpmnth', 'subscript', 'subscriptn3gbpwk', 'subscrit', 'subsequ', 'subtoitl', 'success', 'suck', 'sucker', 'sudden', 'suddenli', 'sudn', 'sue', 'suffer', 'suffici', 'sugabab', 'suganya', 'sugar', 'sugardad', 'suggest', 'suit', 'suitem', 'sullivan', 'sum', 'sum1', 'sumf', 'summer', 'summon', 'sumthin', 'sumthinxx', 'sun', 'sun0819', 'sunday', 'sundayish', 'sunlight', 'sunni', 'sunoco', 'sunroof', 'sunscreen', 'sunshin', 'suntec', 'sup', 'super', 'superb', 'superior', 'supervisor', 'supli', 'supos', 'suppli', 'supplier', 'support', 'supportprovid', 'supportveri', 'suppos', 'suprem', 'suprman', 'sura', 'sure', 'surf', 'surgic', 'surli', 'surnam', 'surpris', 'surrend', 'surround', 'survey', 'surya', 'sutra', 'sux', 'suzi', 'svc', 'sw7', 'sw73ss', 'swalpa', 'swan', 'swann', 'swap', 'swashbuckl', 'swat', 'swatch', 'sway', 'swayz', 'swear', 'sweater', 'sweatter', 'sweet', 'sweetest', 'sweetheart', 'sweeti', 'swell', 'swhrt', 'swim', 'swimsuit', 'swing', 'swiss', 'switch', 'swollen', 'swoop', 'swt', 'swtheart', 'syd', 'syllabu', 'symbol', 'sympathet', 'symptom', 'sync', 'syria', 'syrup', 'system', 't91', 'ta', 'tabl', 'tablet', 'tackl', 'taco', 'tact', 'tactless', 'tadaaaaa', 'tag', 'tahan', 'tai', 'tait', 'taj', 'taka', 'take', 'takecar', 'taken', 'takenonli', 'takin', 'talent', 'talk', 'talkbut', 'talkin', 'tall', 'tallahasse', 'tallent', 'tamilnaduthen', 'tampa', 'tank', 'tantrum', 'tap', 'tape', 'tariff', 'tarot', 'tarpon', 'tast', 'tat', 'tata', 'tattoo', 'tau', 'taught', 'taunton', 'tax', 'taxi', 'taxless', 'taxt', 'taylor', 'tayseertissco', 'tb', 'tbspersolvo', 'tc', 'tcllc', 'tcrw1', 'tcsbcm4235wc1n3xx', 'tcsc', 'tcsstop', 'tddnewsletteremc1couk', 'tea', 'teach', 'teacher', 'teacoffe', 'team', 'tear', 'teas', 'tech', 'technic', 'technolog', 'tee', 'teenag', 'teeth', 'teethi', 'teethif', 'teju', 'tel', 'telephon', 'teletext', 'tell', 'telli', 'tellmiss', 'telphon', 'telugu', 'telugutht', 'temal', 'temp', 'temper', 'templ', 'ten', 'tenant', 'tendenc', 'tenerif', 'tens', 'tension', 'teresa', 'term', 'terminatedw', 'termsappli', 'terri', 'terribl', 'terrif', 'terrorist', 'tesco', 'tessypl', 'test', 'tex', 'texa', 'texd', 'text', 'text82228', 'textand', 'textbook', 'textbuddi', 'textcomp', 'textin', 'textoper', 'textpod', 'textsweekend', 'tgxxrz', 'th', 'thandiyachu', 'thangam', 'thangamit', 'thank', 'thanks2', 'thanksgiv', 'thanku', 'thankyou', 'thanx', 'thanx4', 'thanxxx', 'thasa', 'that', 'that2worzel', 'thatd', 'thatdont', 'thati', 'thatll', 'thatmum', 'thatnow', 'the4th', 'theacus', 'theater', 'theatr', 'thecd', 'thedailydraw', 'thekingshead', 'theme', 'themob', 'themobhit', 'themobyo', 'themp', 'thenwil', 'theoret', 'theori', 'theplac', 'thepub', 'there', 'theredo', 'theregoodnight', 'therel', 'therer', 'therexx', 'theseday', 'theseyour', 'thesi', 'thesmszonecom', 'thewend', 'theyll', 'theyr', 'thgt', 'thi', 'thia', 'thin', 'thing', 'thinghow', 'think', 'thinkin', 'thinkthi', 'thinl', 'thirunelvali', 'thisdon', 'thk', 'thkin', 'thm', 'thnk', 'thnq', 'thnx', 'tho', 'thoso', 'thot', 'thou', 'though', 'thought', 'thoughtsi', 'thousand', 'thout', 'thread', 'threat', 'three', 'threw', 'thriller', 'throat', 'throw', 'throwin', 'thrown', 'thru', 'thrurespect', 'tht', 'thu', 'thuglyf', 'thur', 'thursday', 'thx', 'ti', 'tick', 'ticket', 'tiempo', 'tiger', 'tight', 'tightli', 'tigress', 'tih', 'tiim', 'til', 'till', 'tim', 'time', 'timedhoni', 'timegud', 'timehop', 'timeslil', 'timey', 'timeyour', 'timi', 'timin', 'tini', 'tip', 'tire', 'tirunelvai', 'tirunelvali', 'tirupur', 'tisscotays', 'titl', 'titleso', 'tiwari', 'tix', 'tiz', 'tke', 'tkt', 'tlk', 'tm', 'tming', 'tmobil', 'tmorrowpl', 'tmr', 'tmrw', 'tmw', 'tnc', 'toa', 'toaday', 'tobacco', 'tobe', 'tocallshal', 'toclaim', 'today', 'todaybut', 'todaydo', 'todayfrom', 'todaygood', 'todayh', 'todaysundaysunday', 'todo', 'tog', 'togeth', 'tohar', 'toilet', 'tok', 'toke', 'token', 'tol', 'told', 'toldsh', 'toledo', 'toler', 'toleratbc', 'toll', 'tom', 'tomarrow', 'tome', 'tomeandsaidthi', 'tomo', 'tomoc', 'tomorro', 'tomorrow', 'tomorrowcal', 'tomorrowtoday', 'tomorw', 'ton', 'tone', 'tones2u', 'tones2youcouk', 'tonesrepli', 'tonex', 'tonght', 'tongu', 'tonight', 'tonit', 'tonitebusi', 'toniteth', 'tonsolitusaswel', 'took', 'tookplac', 'tool', 'toolet', 'tooo', 'toopray', 'toot', 'toothpast', 'tootsi', 'top', 'topic', 'topicsorri', 'toplay', 'toppoli', 'tor', 'torch', 'torrent', 'tortilla', 'tortur', 'tosend', 'toshiba', 'toss', 'tot', 'total', 'tote', 'touch', 'tough', 'toughest', 'tour', 'toward', 'town', 'towncud', 'towndontmatt', 'toxic', 'toyota', 'tp', 'track', 'trackmarqu', 'trade', 'tradit', 'traffic', 'train', 'trainner', 'tram', 'tranquil', 'transact', 'transcrib', 'transfer', 'transferacc', 'transfr', 'transport', 'trash', 'trauma', 'trav', 'travel', 'treacl', 'treadmil', 'treasur', 'treat', 'treatin', 'trebl', 'tree', 'trek', 'trend', 'tri', 'trial', 'trip', 'tripl', 'trishul', 'triumph', 'tron', 'troubl', 'troubleshoot', 'trouser', 'trubl', 'truck', 'true', 'truekdo', 'truffl', 'truli', 'truro', 'trust', 'truth', 'tryin', 'trywal', 'ts', 'tsandc', 'tsc', 'tscs08714740323', 'tscs087147403231winawkage16', 'tshirt', 'tsunami', 'tt', 'ttyl', 'tue', 'tuesday', 'tui', 'tuition', 'tul', 'tulip', 'tund', 'tune', 'tunji', 'turkey', 'turn', 'tuth', 'tv', 'tvhe', 'tvlol', 'twat', 'twelv', 'twenti', 'twice', 'twigg', 'twilight', 'twin', 'twink', 'twitter', 'two', 'tx', 'txt', 'txt250com', 'txtauction', 'txtauctiontxt', 'txtin', 'txting', 'txtjourney', 'txtno', 'txtx', 'tyler', 'type', 'typelyk', 'typic', 'u', 'u2moro', 'u4', 'uawakefeellikw', 'ubandu', 'ubi', 'ucal', 'ufind', 'ugadi', 'ugh', 'ugo', 'uh', 'uhhhhrmm', 'uif', 'uin', 'ujhhhhhhh', 'uk', 'ukmobiled', 'ukp2000', 'ull', 'ultim', 'ultimatum', 'um', 'umma', 'ummmawil', 'ummmmmaah', 'un', 'unabl', 'unbeliev', 'uncl', 'unclaim', 'uncomfort', 'uncondit', 'unconsci', 'unconvinc', 'uncount', 'uncut', 'underdtand', 'understand', 'understood', 'underwear', 'undrstnd', 'undrstndng', 'unemploy', 'unev', 'unfold', 'unfortun', 'unfortuntli', 'unhappi', 'uni', 'unicef', 'uniform', 'unintent', 'uniqu', 'uniquei', 'unit', 'univ', 'univers', 'unknown', 'unless', 'unlik', 'unlimit', 'unmit', 'unnecessarili', 'unni', 'unrecogn', 'unredeem', 'unsecur', 'unsold', 'unsoldmik', 'unsoldnow', 'unspoken', 'unsub', 'unsubscrib', 'unusu', 'uothrwis', 'up', 'up4', 'upcharg', 'upd8', 'updat', 'updatenow', 'upgrad', 'upgrdcentr', 'uphad', 'upload', 'upnot', 'upon', 'upset', 'upseti', 'upsetit', 'upstair', 'upto', 'uptown', 'upyeh', 'ur', 'ure', 'urfeel', 'urgent', 'urgentbut', 'urgentlyit', 'urgh', 'urgnt', 'urgoin', 'urgran', 'urin', 'url', 'urmomi', 'urn', 'urself', 'us', 'usb', 'usc', 'uscedu', 'use', 'useless', 'user', 'usf', 'usget', 'usher', 'uslet', 'usml', 'usno', 'uso', 'usp', 'usual', 'usualiam', 'uteru', 'utter', 'uu', 'uup', 'uv', 'uve', 'uwana', 'uwant', 'uworld', 'uxxxx', 'v', 'vaazhthukk', 'vagu', 'vai', 'vale', 'valentin', 'valid', 'valid12hr', 'valu', 'valuabl', 'valuemorn', 'varaya', 'vargu', 'vari', 'variou', 'varma', 'vasai', 'vat', 'vatian', 'vava', 'vco', 'vday', 'vega', 'veget', 'veggi', 'vehicl', 'velacheri', 'velli', 'velusami', 'venaam', 'venugop', 'verifi', 'version', 'versu', 'vettam', 'vewi', 'via', 'vibrant', 'vibrat', 'vic', 'victor', 'victoria', 'vid', 'video', 'videochat', 'videop', 'videophon', 'videosound', 'videosounds2', 'vidnot', 'view', 'vijay', 'vijaykanth', 'vikki', 'vikkyim', 'vilikkamt', 'vill', 'villa', 'villag', 'vinobanagar', 'violat', 'violenc', 'violet', 'vip', 'virgil', 'virgin', 'virtual', 'visa', 'visionsmscom', 'visit', 'visitne', 'visitor', 'vital', 'vitamin', 'viva', 'vivek', 'vivekanand', 'viveki', 'vl', 'vldo', 'voda', 'vodafon', 'vodka', 'voic', 'voicemail', 'voila', 'volcano', 'vomit', 'vomitin', 'vote', 'voucher', 'voucherstext', 'vpist', 'vpod', 'vri', 'vs', 'vth', 'vtire', 'vu', 'w', 'w111wx', 'w14rg', 'w1a', 'w1j', 'w1t1ji', 'w4', 'w45wq', 'w8in', 'wa', 'wa14', 'waaaat', 'wad', 'wadebridgei', 'wah', 'wahala', 'wahay', 'wahe', 'waheeda', 'wahleykkumshar', 'waht', 'wait', 'waiti', 'waitin', 'waitshould', 'waitu', 'wake', 'wale', 'walik', 'walk', 'walkabout', 'walkin', 'wall', 'wallet', 'wallpap', 'wallpaperal', 'walmart', 'walsal', 'wamma', 'wan', 'wan2', 'wana', 'wanna', 'wannatel', 'want', 'want2com', 'wap', 'waqt', 'warm', 'warn', 'warner', 'warranti', 'warwick', 'washob', 'wasnt', 'wast', 'wat', 'watch', 'watchin', 'watchng', 'wate', 'water', 'watev', 'watevr', 'watll', 'watrdayno', 'watt', 'wave', 'way', 'way2smscom', 'waythi', 'wc', 'wc1n', 'wc1n3xx', 'weak', 'weapon', 'wear', 'weasel', 'weather', 'web', 'web2mobil', 'webadr', 'webeburnin', 'webpag', 'websit', 'websitenow', 'wed', 'weddin', 'weddingfriend', 'wedlunch', 'wednesday', 'wee', 'weed', 'weeddefici', 'week', 'weekday', 'weekend', 'weekli', 'weekstop', 'weigh', 'weight', 'weighthaha', 'weightloss', 'weird', 'weirdest', 'weirdi', 'weirdo', 'weiyi', 'welcom', 'well', 'wellda', 'welli', 'welltak', 'wellyou', 'welp', 'wen', 'wendi', 'wenev', 'went', 'wenwecan', 'wer', 'werear', 'werebor', 'werent', 'wereth', 'wesley', 'west', 'western', 'westlif', 'westonzoyland', 'westshor', 'wet', 'wetherspoon', 'weve', 'wewa', 'whassup', 'what', 'whatev', 'whatsup', 'wheat', 'wheel', 'wheellock', 'when', 'whenev', 'whenevr', 'whenr', 'whenwher', 'where', 'wherear', 'wherebtw', 'wherev', 'wherevr', 'wherr', 'whether', 'whileamp', 'whilltak', 'whisper', 'white', 'whn', 'who', 'whole', 'whore', 'whose', 'whr', 'wi', 'wick', 'wicket', 'wicklow', 'wid', 'widelivecomindex', 'wif', 'wife', 'wifedont', 'wifehow', 'wifi', 'wihtuot', 'wikipediacom', 'wil', 'wild', 'wildest', 'wildlif', 'will', 'willpow', 'win', 'win150ppmx3age16', 'wind', 'windi', 'window', 'wine', 'wing', 'winner', 'winnersclub', 'winterston', 'wipe', 'wipro', 'wiproy', 'wire3net', 'wisdom', 'wise', 'wish', 'wishin', 'wishlist', 'wiskey', 'wit', 'withdraw', 'wither', 'within', 'without', 'witin', 'witot', 'witout', 'wiv', 'wizzl', 'wk', 'wkend', 'wkent150p16', 'wkg', 'wkli', 'wknd', 'wktxt', 'wlcome', 'wld', 'wmlid1b6a5ecef91ff937819firsttrue180430jul05', 'wmlid820554ad0a1705572711firsttru', 'wn', 'wnevr', 'wnt', 'wo', 'woah', 'wocay', 'woke', 'woken', 'woman', 'womdarful', 'women', 'wondar', 'wondarful', 'wonder', 'wont', 'woo', 'wood', 'woodland', 'woohoo', 'woot', 'woould', 'woozl', 'worc', 'word', 'wordcollect', 'wordnot', 'wordsevri', 'wordstart', 'work', 'workag', 'workand', 'workin', 'worklov', 'workout', 'world', 'worldgnun', 'worldmay', 'worldveri', 'worm', 'worri', 'worriedx', 'worryc', 'worryus', 'wors', 'worst', 'worth', 'worthless', 'wot', 'wotu', 'wotz', 'woul', 'would', 'woulda', 'wouldnt', 'wound', 'wow', 'wquestion', 'wrc', 'wreck', 'wrench', 'wright', 'write', 'writh', 'wrk', 'wrki', 'wrkin', 'wrking', 'wrld', 'wrnog', 'wrong', 'wrongli', 'wrongtak', 'wrote', 'ws', 'wt', 'wtc', 'wtf', 'wth', 'wthout', 'wud', 'wudnt', 'wuld', 'wuldnt', 'wun', 'www07781482378com', 'www4tcbiz', 'www80488biz', 'wwwapplausestorecom', 'wwwareyouuniquecouk', 'wwwasjesuscom', 'wwwb4utelecom', 'wwwbridalpetticoatdreamscouk', 'wwwcashbincouk', 'wwwclubmobycom', 'wwwclubzedcouk', 'wwwcnupdatescomnewslett', 'wwwcomuknet', 'wwwdbuknet', 'wwwflirtpartyu', 'wwwfullonsmscom', 'wwwgambtv', 'wwwgetzedcouk', 'wwwidewcom', 'wwwldewcom', 'wwwldewcom1win150ppmx3age16', 'wwwldewcom1win150ppmx3age16subscript', 'wwwldewcomsubs161win150ppmx3', 'wwwmovietriviatv', 'wwwmusictrivianet', 'wwworangecoukow', 'wwwphb1com', 'wwwregalportfoliocouk', 'wwwringtonekingcouk', 'wwwringtonescouk', 'wwwrtfsphostingcom', 'wwwsantacallingcom', 'wwwshortbreaksorguk', 'wwwsmsacubootydeli', 'wwwsmsacugoldvik', 'wwwsmsacuhmmross', 'wwwsmsacunat27081980', 'wwwsmsacunatalie2k9', 'wwwsmsconet', 'wwwtcbiz', 'wwwtelediscountcouk', 'wwwtextcompcom', 'wwwtextpodnet', 'wwwtklscom', 'wwwtxt2shopcom', 'wwwtxt43com', 'wwwtxt82228com', 'wwwtxttowincouk', 'wwwwin82050couk', 'wyli', 'x', 'x2', 'x29', 'x49', 'x49your', 'xafter', 'xam', 'xavier', 'xchat', 'xclusiveclubsaisai', 'xin', 'xma', 'xnet', 'xoxo', 'xt', 'xuhui', 'xx', 'xxsp', 'xxuk', 'xxx', 'xxxmobilemovieclub', 'xxxmobilemovieclubcomnqjkgighjjgcbl', 'xxxx', 'xxxxx', 'xxxxxx', 'xxxxxxx', 'xxxxxxxx', 'xxxxxxxxxxxxxx', 'xy', 'y87', 'ya', 'yago', 'yah', 'yahoo', 'yalrigu', 'yalru', 'yam', 'yan', 'yar', 'yard', 'yavnt', 'yaxx', 'yaxxx', 'yay', 'yck', 'yday', 'ye', 'yeah', 'yeahand', 'year', 'yeesh', 'yeh', 'yell', 'yellow', 'yelowi', 'yen', 'yeovil', 'yep', 'yer', 'yes165', 'yes434', 'yes440', 'yes762', 'yes910', 'yesbut', 'yesfrom', 'yesgauti', 'yesh', 'yesher', 'yesim', 'yesmum', 'yessura', 'yest', 'yesterday', 'yet', 'yetti', 'yetund', 'yi', 'yifeng', 'yiju', 'yijuehotmailcom', 'ym', 'ymca', 'yo', 'yoga', 'yogasana', 'yoher', 'yor', 'yorg', 'youani', 'youcarlo', 'youclean', 'youd', 'youdearwith', 'youdo', 'youhow', 'youi', 'youkwher', 'yould', 'youll', 'youmi', 'youmoney', 'young', 'younger', 'youphon', 'your', 'yourinclus', 'yourjob', 'youso', 'youthat', 'youto', 'youuuuu', 'youv', 'youwanna', 'youwhen', 'yovil', 'yowif', 'yoyyooo', 'yr', 'ystrdayic', 'yummi', 'yummmm', 'yun', 'yunni', 'yuo', 'yuou', 'yup', 'yupz', 'ywhere', 'z', 'zac', 'zaher', 'zealand', 'zebra', 'zed', 'zero', 'zhong', 'zindgi', 'zoe', 'zogtoriu', 'zoom', 'zouk', 'zyada', 'é', 'ü', 'üll', '〨ud']\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "tfidf_vect = TfidfVectorizer(analyzer=clean_text)\n",
    "X_tfidf = tfidf_vect.fit_transform(data['body_text'])\n",
    "print(X_tfidf.shape)\n",
    "print(tfidf_vect.get_feature_names())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Apply TfidfVectorizer to smaller sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(20, 192)\n",
      "['08002986030', '08452810075over18', '09061701461', '1', '100', '100000', '11', '12', '150pday', '16', '2', '20000', '2005', '21st', '3', '4', '4403ldnw1a7rw18', '4txtú120', '6day', '81010', '87077', '87121', '87575', '9', '900', 'aft', 'aid', 'alreadi', 'alright', 'anymor', 'appli', 'ard', 'around', 'b', 'brother', 'call', 'caller', 'callertun', 'camera', 'cash', 'chanc', 'claim', 'click', 'co', 'code', 'colour', 'comin', 'comp', 'copi', 'cost', 'credit', 'cri', 'csh11', 'cup', 'custom', 'da', 'date', 'dont', 'eg', 'eh', 'england', 'enough', 'entitl', 'entri', 'even', 'fa', 'feel', 'ffffffffff', 'final', 'fine', 'finish', 'first', 'free', 'friend', 'go', 'goalsteam', 'goe', 'gonna', 'gota', 'ha', 'hl', 'home', 'hour', 'httpwap', 'im', 'info', 'ive', 'jackpot', 'joke', 'k', 'kim', 'kl341', 'lar', 'latest', 'lccltd', 'like', 'link', 'live', 'lor', 'lunch', 'macedonia', 'make', 'may', 'meet', 'mell', 'membership', 'messag', 'minnaminungint', 'miss', 'mobil', 'month', 'nah', 'name', 'nation', 'naughti', 'network', 'news', 'next', 'nurungu', 'oh', 'oru', 'patent', 'pay', 'per', 'pobox', 'poboxox36504w45wq', 'pound', 'press', 'prize', 'questionstd', 'r', 'ratetc', 'receiv', 'receivea', 'rememb', 'repli', 'request', 'reward', 'scotland', 'select', 'send', 'serious', 'set', 'six', 'smth', 'soon', 'sooner', 'speak', 'spell', 'stock', 'str', 'stuff', 'sunday', 'talk', 'tc', 'team', 'text', 'think', 'though', 'tkt', 'today', 'tonight', 'treat', 'tri', 'trywal', 'tsandc', 'txt', 'u', 'updat', 'ur', 'urgent', 'use', 'usf', 'v', 'valid', 'valu', 'vettam', 'want', 'wap', 'watch', 'way', 'week', 'wet', 'win', 'winner', 'wkli', 'word', 'wwwdbuknet', 'xxxmobilemovieclub', 'xxxmobilemovieclubcomnqjkgighjjgcbl', 'ye', 'ü']\n"
     ]
    }
   ],
   "source": [
    "data_sample = data[0:20]\n",
    "\n",
    "tfidf_vect_sample = TfidfVectorizer(analyzer=clean_text)\n",
    "X_tfidf_sample = tfidf_vect_sample.fit_transform(data_sample['body_text'])\n",
    "print(X_tfidf_sample.shape)\n",
    "print(tfidf_vect_sample.get_feature_names())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Vectorizers output sparse matrices\n",
    "\n",
    "_**Sparse Matrix**: A matrix in which most entries are 0. In the interest of efficient storage, a sparse matrix will be stored by only storing the locations of the non-zero elements._"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
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       "    <tr>\n",
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       "    <tr>\n",
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       "    <tr>\n",
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       "    <tr>\n",
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       "    <tr>\n",
       "      <th>18</th>\n",
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       "<p>20 rows × 192 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    08002986030  08452810075over18  09061701461         1       100    100000  \\\n",
       "0      0.000000           0.198986     0.000000  0.000000  0.000000  0.000000   \n",
       "1      0.000000           0.000000     0.000000  0.000000  0.000000  0.000000   \n",
       "2      0.000000           0.000000     0.000000  0.000000  0.000000  0.000000   \n",
       "3      0.000000           0.000000     0.000000  0.000000  0.000000  0.000000   \n",
       "4      0.000000           0.000000     0.000000  0.000000  0.000000  0.000000   \n",
       "5      0.000000           0.000000     0.231645  0.000000  0.000000  0.000000   \n",
       "6      0.197682           0.000000     0.000000  0.000000  0.000000  0.000000   \n",
       "7      0.000000           0.000000     0.000000  0.000000  0.000000  0.000000   \n",
       "8      0.000000           0.000000     0.000000  0.000000  0.224905  0.000000   \n",
       "9      0.000000           0.000000     0.000000  0.252972  0.000000  0.252972   \n",
       "10     0.000000           0.000000     0.000000  0.000000  0.000000  0.000000   \n",
       "11     0.000000           0.000000     0.000000  0.000000  0.000000  0.000000   \n",
       "12     0.000000           0.000000     0.000000  0.000000  0.000000  0.000000   \n",
       "13     0.000000           0.000000     0.000000  0.000000  0.000000  0.000000   \n",
       "14     0.000000           0.000000     0.000000  0.000000  0.000000  0.000000   \n",
       "15     0.000000           0.000000     0.000000  0.000000  0.000000  0.000000   \n",
       "16     0.000000           0.000000     0.000000  0.000000  0.000000  0.000000   \n",
       "17     0.000000           0.000000     0.000000  0.000000  0.000000  0.000000   \n",
       "18     0.000000           0.000000     0.000000  0.000000  0.000000  0.000000   \n",
       "19     0.000000           0.000000     0.000000  0.000000  0.000000  0.000000   \n",
       "\n",
       "          11        12   150pday        16    ...          wet       win  \\\n",
       "0   0.000000  0.000000  0.000000  0.000000    ...     0.000000  0.174912   \n",
       "1   0.000000  0.000000  0.000000  0.000000    ...     0.000000  0.000000   \n",
       "2   0.000000  0.000000  0.000000  0.000000    ...     0.000000  0.000000   \n",
       "3   0.000000  0.000000  0.000000  0.000000    ...     0.000000  0.000000   \n",
       "4   0.000000  0.000000  0.000000  0.000000    ...     0.000000  0.000000   \n",
       "5   0.000000  0.231645  0.000000  0.000000    ...     0.000000  0.000000   \n",
       "6   0.197682  0.000000  0.000000  0.000000    ...     0.000000  0.000000   \n",
       "7   0.000000  0.000000  0.000000  0.000000    ...     0.000000  0.000000   \n",
       "8   0.000000  0.000000  0.224905  0.197695    ...     0.000000  0.197695   \n",
       "9   0.000000  0.000000  0.000000  0.000000    ...     0.000000  0.000000   \n",
       "10  0.000000  0.000000  0.000000  0.000000    ...     0.000000  0.000000   \n",
       "11  0.000000  0.000000  0.000000  0.000000    ...     0.000000  0.000000   \n",
       "12  0.000000  0.000000  0.000000  0.000000    ...     0.291197  0.000000   \n",
       "13  0.000000  0.000000  0.000000  0.000000    ...     0.000000  0.000000   \n",
       "14  0.000000  0.000000  0.000000  0.185730    ...     0.000000  0.000000   \n",
       "15  0.000000  0.000000  0.000000  0.000000    ...     0.000000  0.000000   \n",
       "16  0.000000  0.000000  0.000000  0.000000    ...     0.000000  0.000000   \n",
       "17  0.000000  0.000000  0.000000  0.000000    ...     0.000000  0.000000   \n",
       "18  0.000000  0.000000  0.000000  0.000000    ...     0.000000  0.000000   \n",
       "19  0.000000  0.000000  0.000000  0.000000    ...     0.000000  0.000000   \n",
       "\n",
       "      winner      wkli      word  wwwdbuknet  xxxmobilemovieclub  \\\n",
       "0   0.000000  0.198986  0.000000    0.000000            0.000000   \n",
       "1   0.000000  0.000000  0.000000    0.000000            0.000000   \n",
       "2   0.000000  0.000000  0.000000    0.000000            0.000000   \n",
       "3   0.000000  0.000000  0.000000    0.000000            0.000000   \n",
       "4   0.000000  0.000000  0.000000    0.000000            0.000000   \n",
       "5   0.231645  0.000000  0.000000    0.000000            0.000000   \n",
       "6   0.000000  0.000000  0.000000    0.000000            0.000000   \n",
       "7   0.000000  0.000000  0.000000    0.000000            0.000000   \n",
       "8   0.000000  0.000000  0.000000    0.000000            0.000000   \n",
       "9   0.000000  0.000000  0.252972    0.252972            0.000000   \n",
       "10  0.000000  0.000000  0.000000    0.000000            0.272652   \n",
       "11  0.000000  0.000000  0.000000    0.000000            0.000000   \n",
       "12  0.000000  0.000000  0.000000    0.000000            0.000000   \n",
       "13  0.000000  0.000000  0.000000    0.000000            0.000000   \n",
       "14  0.000000  0.000000  0.000000    0.000000            0.000000   \n",
       "15  0.000000  0.000000  0.000000    0.000000            0.000000   \n",
       "16  0.000000  0.000000  0.000000    0.000000            0.000000   \n",
       "17  0.000000  0.000000  0.000000    0.000000            0.000000   \n",
       "18  0.000000  0.000000  0.000000    0.000000            0.000000   \n",
       "19  0.000000  0.000000  0.000000    0.000000            0.000000   \n",
       "\n",
       "    xxxmobilemovieclubcomnqjkgighjjgcbl        ye         ü  \n",
       "0                              0.000000  0.000000  0.000000  \n",
       "1                              0.000000  0.000000  0.000000  \n",
       "2                              0.000000  0.000000  0.000000  \n",
       "3                              0.000000  0.000000  0.000000  \n",
       "4                              0.000000  0.000000  0.000000  \n",
       "5                              0.000000  0.000000  0.000000  \n",
       "6                              0.000000  0.000000  0.000000  \n",
       "7                              0.000000  0.000000  0.000000  \n",
       "8                              0.000000  0.000000  0.000000  \n",
       "9                              0.000000  0.000000  0.000000  \n",
       "10                             0.272652  0.000000  0.000000  \n",
       "11                             0.000000  0.000000  0.000000  \n",
       "12                             0.000000  0.291197  0.000000  \n",
       "13                             0.000000  0.000000  0.000000  \n",
       "14                             0.000000  0.000000  0.000000  \n",
       "15                             0.000000  0.000000  0.000000  \n",
       "16                             0.000000  0.000000  0.000000  \n",
       "17                             0.000000  0.000000  0.377964  \n",
       "18                             0.000000  0.000000  0.000000  \n",
       "19                             0.000000  0.000000  0.000000  \n",
       "\n",
       "[20 rows x 192 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_tfidf_df = pd.DataFrame(X_tfidf_sample.toarray())\n",
    "X_tfidf_df.columns = tfidf_vect_sample.get_feature_names()\n",
    "X_tfidf_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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