NLP ML (Built-In)

9 Apply TfidfVectorizer.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Apply CountVectorizer (N-Grams)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "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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'09058094597', '09058094599', '09058095107', '09058095201', '09058097189', '09058097218', '09058098002', '09058099801', '09061104276', '09061104283', '09061209465', '09061213237', '09061221061', '09061221066', '09061701444', '09061701461', '09061701851', '09061701939', '09061702893', '09061743386', '09061743806', '09061743810', '09061743811', '09061744553', '09061749602', '09061790121', '09061790125', '09061790126', '09063440451', '09063442151', '09063458130', '0906346330', '09064011000', '09064012103', '09064012160', '09064015307', '09064017295', '09064017305', '09064018838', '09064019014', '09064019788', '09065069120', '09065069154', '09065171142stopsms08', '09065171142stopsms08718727870150ppm', '09065174042', '09065394514', '09065394973', '09065989180', '09065989182', '09066350750', '09066358152', '09066358361', '09066361921', '09066362206', '09066362220', '09066362231', '09066364311', '09066364349', '09066364589', '09066368327', '09066368470', '09066368753', '09066380611', '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', 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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', '750', '75000', '7548', '7634', '7684', '7732584351', '78', '786', '7876150ppm', '78pmin', '79', '7am', '7cfca1a', '7children', '7ish', '7mahav', '7oz', '7pm', '7romant', '7shi', '7th', '7w', '7z', '8', '80', '800', '8000930705', '80062', '8007', '80082', '80086', '80122300pwk', '80155', '80160', '80182', '8027', '80488', '80488biz', '80608', '8077', '80878', '81010', '81151', '81303', '81618', '816183', '82242', '82277', '82277unsub', '82324', '82468', '830', '83021', '83039', '83049', '83110', '83118', '83222', '83332pleas', '83338', '83355', '83370', '83383', '83435', '83600', '83738', '84', '84025', '84122', '84128', '84128custcar', '84199', '84484', '85', '850', '85023', '85069', '85222', '85233', '8552', '85555', '86021', '861', '863', '864233', '86688', '86888', '87021', '87066', '87070', '87077', '87121', '87131', '8714714', '87239', '87575', '8800', '88039', '88039skilgmetscs087147403231winawkage16', '88066', '88088', '88222', '8830', '88600', '88800', '8883', '88877', 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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', 'albi', 'album', 'albumquit', 'alcohol', 'aldrin', 'alert', 'alertfrom', 'alett', 'alex', 'alfi', 'algarv', 'algebra', 'algorithm', 'ali', 'alian', 'alibi', 'aliv', 'alivebett', 'all', 'allah', 'allahmeet', 'allahrakhesh', 'allalo', 'allday', 'allo', 'allow', 'almost', 'alon', 'along', 'alot', 'alreadi', 'alreadysabarish', 'alright', 'alrightokay', 'alrit', 'alritehav', 'also', 'alsoor', 'alter', 'alternativehop', 'although', 'alwa', 'alway', 'alwi', 'am', 'amanda', 'amaz', 'ambiti', 'ambrithmaduraimet', 'american', 'ami', 'amigo', 'amk', 'ammaelif', 'ammo', 'amnow', 'among', 'amongst', 'amount', 'amp', 'amplikat', 'amrca', 'amrita', 'amt', 'amus', 'amx', 'an', 'ana', 'anal', 'analysi', 'anand', 'anderson', 'andor', 'andr', 'andrewsboy', 'andro', 'angel', 'angri', 'anim', 'anji', 'anjola', 'anna', 'anni', 'anniversari', 'annonc', 'announc', 'annoy', 'annoyin', 'anonym', 'anot', 'anoth', 'ansr', 'answer', 'answerin', 'answr', 'antelop', 'anthoni', 'anti', 'antibiot', 'anybodi', 'anyhow', 'anymor', 'anyon', 'anyplac', 'anyth', 'anythi', 'anythin', 'anythingtomorrow', 'anytim', 'anyway', 'anywher', 'aom', 'apart', 'ape', 'apeshit', 'aphex', 'apnt', 'apo', 'apolog', 'apologet', 'apologis', 'app', 'appar', 'appeal', 'appear', 'appendix', 'appi', 'applebe', 'applespairsal', 'appli', 'applic', 'apply2', 'appoint', 'appreci', 'approach', 'appropri', 'approv', 'approx', 'appt', 'april', 'aproach', 'apt', 'aptitud', 'aquariu', 'ar', 'arab', 'arabian', 'arcad', 'archiv', 'ard', 'ardé', 'area', 'arent', 'arestaur', 'aretak', 'argentina', 'argh', 'argu', 'argument', 'ari', 'aris', 'arithmet', 'arm', 'armand', 'armenia', 'arng', 'arngd', 'arnt', 'around', 'aroundn', 'arpraveesh', 'arr', 'arrang', 'arrest', 'arriv', 'arrow', 'arsen', 'art', 'arti', 'artist', 'arul', 'arun', 'asa', 'asap', 'asapok', 'asda', 'ash', 'ashley', 'ashwini', 'asia', 'asian', 'ask', 'askd', 'askin', 'aslamalaikkuminsha', 'asleep', 'aspect', 'ass', 'assess', 'asshol', 'assist', 'associ', 'assum', 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'bak', 'bakra', 'bakrid', 'balanc', 'ball', 'baller', 'balloon', 'bam', 'bambl', 'ban', 'band', 'bandag', 'bang', 'bangb', 'bangbab', 'bani', 'bank', 'banneduk', 'banter', 'bao', 'bar', 'barbi', 'barcelona', 'bare', 'bari', 'barkley', 'barm', 'barolla', 'barrel', 'barri', 'base', 'bash', 'basic', 'basket', 'basketbal', 'basqihav', 'bat', 'batch', 'batchlor', 'bath', 'bathroom', 'batsman', 'batt', 'batteri', 'battl', 'bawl', 'bay', 'bb', 'bbc', 'bbdelux', 'bbdpooja', 'bbdtht', 'bblue', 'bbq', 'bc', 'bcaz', 'bck', 'bcm', 'bcm1896wc1n3xx', 'bcm4284', 'bcmsfwc1n3xx', 'bcoz', 'bcozi', 'bcum', 'bcz', 'bday', 'beach', 'bead', 'bear', 'beat', 'beauti', 'beautifulmay', 'bec', 'becau', 'becausethey', 'becom', 'becoz', 'becz', 'bed', 'bedbut', 'bedreal', 'bedrm', 'bedrm900', 'bedroom', 'bedroomlov', 'beeen', 'beehoon', 'beendrop', 'beer', 'beerag', 'beerr', 'befor', 'beforehand', 'beforew', 'beg', 'beggar', 'begin', 'begun', 'behalf', 'behav', 'behind', 'bein', 'believ', 'beliv', 'bell', 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     ]
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "\n",
    "ngram_vect = CountVectorizer(ngram_range=(2,2),analyzer=clean_text) # It applies only bigram vectorizer\n",
    "X_counts = ngram_vect.fit_transform(data['body_text'])\n",
    "print(X_counts.shape)\n",
    "print(ngram_vect.get_feature_names())"
   ]
  }
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