In [1]:

    

def pretty_print_review_and_label(i):
    print(labels[i] + "\t:\t" + reviews[i][:80] + "...")

g = open('reviews.txt','r') # What we know!
reviews = list(map(lambda x:x[:-1],g.readlines()))
g.close()

g = open('labels.txt','r') # What we WANT to know!
labels = list(map(lambda x:x[:-1].upper(),g.readlines()))
g.close()


    

In [2]:

    

len(reviews)


    

    
Out[2]:





25000

In [3]:

    

reviews[0]


    

    
Out[3]:





'bromwell high is a cartoon comedy . it ran at the same time as some other programs about school life  such as  teachers  . my   years in the teaching profession lead me to believe that bromwell high  s satire is much closer to reality than is  teachers  . the scramble to survive financially  the insightful students who can see right through their pathetic teachers  pomp  the pettiness of the whole situation  all remind me of the schools i knew and their students . when i saw the episode in which a student repeatedly tried to burn down the school  i immediately recalled . . . . . . . . . at . . . . . . . . . . high . a classic line inspector i  m here to sack one of your teachers . student welcome to bromwell high . i expect that many adults of my age think that bromwell high is far fetched . what a pity that it isn  t   '

In [4]:

    

labels[0]


    

    
Out[4]:





'POSITIVE'

In [5]:

    

print("labels.txt \t : \t reviews.txt\n")
pretty_print_review_and_label(2137)
pretty_print_review_and_label(12816)
pretty_print_review_and_label(6267)
pretty_print_review_and_label(21934)
pretty_print_review_and_label(5297)
pretty_print_review_and_label(4998)


    

    




labels.txt 	 : 	 reviews.txt

NEGATIVE	:	this movie is terrible but it has some good effects .  ...
POSITIVE	:	adrian pasdar is excellent is this film . he makes a fascinating woman .  ...
NEGATIVE	:	comment this movie is impossible . is terrible  very improbable  bad interpretat...
POSITIVE	:	excellent episode movie ala pulp fiction .  days   suicides . it doesnt get more...
NEGATIVE	:	if you haven  t seen this  it  s terrible . it is pure trash . i saw this about ...
POSITIVE	:	this schiffer guy is a real genius  the movie is of excellent quality and both e...

In [6]:

    

from collections import Counter
import numpy as np


    

In [7]:

    

positive_counts = Counter()
negative_counts = Counter()
total_counts = Counter()


    

In [8]:

    

for i in range(len(reviews)):
    if(labels[i] == 'POSITIVE'):
        for word in reviews[i].split(" "):
            positive_counts[word] += 1
            total_counts[word] += 1
    else:
        for word in reviews[i].split(" "):
            negative_counts[word] += 1
            total_counts[word] += 1


    

In [9]:

    

positive_counts.most_common()


    

    
Out[9]:





[('', 550468),
 ('the', 173324),
 ('.', 159654),
 ('and', 89722),
 ('a', 83688),
 ('of', 76855),
 ('to', 66746),
 ('is', 57245),
 ('in', 50215),
 ('br', 49235),
 ('it', 48025),
 ('i', 40743),
 ('that', 35630),
 ('this', 35080),
 ('s', 33815),
 ('as', 26308),
 ('with', 23247),
 ('for', 22416),
 ('was', 21917),
 ('film', 20937),
 ('but', 20822),
 ('movie', 19074),
 ('his', 17227),
 ('on', 17008),
 ('you', 16681),
 ('he', 16282),
 ('are', 14807),
 ('not', 14272),
 ('t', 13720),
 ('one', 13655),
 ('have', 12587),
 ('be', 12416),
 ('by', 11997),
 ('all', 11942),
 ('who', 11464),
 ('an', 11294),
 ('at', 11234),
 ('from', 10767),
 ('her', 10474),
 ('they', 9895),
 ('has', 9186),
 ('so', 9154),
 ('like', 9038),
 ('about', 8313),
 ('very', 8305),
 ('out', 8134),
 ('there', 8057),
 ('she', 7779),
 ('what', 7737),
 ('or', 7732),
 ('good', 7720),
 ('more', 7521),
 ('when', 7456),
 ('some', 7441),
 ('if', 7285),
 ('just', 7152),
 ('can', 7001),
 ('story', 6780),
 ('time', 6515),
 ('my', 6488),
 ('great', 6419),
 ('well', 6405),
 ('up', 6321),
 ('which', 6267),
 ('their', 6107),
 ('see', 6026),
 ('also', 5550),
 ('we', 5531),
 ('really', 5476),
 ('would', 5400),
 ('will', 5218),
 ('me', 5167),
 ('had', 5148),
 ('only', 5137),
 ('him', 5018),
 ('even', 4964),
 ('most', 4864),
 ('other', 4858),
 ('were', 4782),
 ('first', 4755),
 ('than', 4736),
 ('much', 4685),
 ('its', 4622),
 ('no', 4574),
 ('into', 4544),
 ('people', 4479),
 ('best', 4319),
 ('love', 4301),
 ('get', 4272),
 ('how', 4213),
 ('life', 4199),
 ('been', 4189),
 ('because', 4079),
 ('way', 4036),
 ('do', 3941),
 ('made', 3823),
 ('films', 3813),
 ('them', 3805),
 ('after', 3800),
 ('many', 3766),
 ('two', 3733),
 ('too', 3659),
 ('think', 3655),
 ('movies', 3586),
 ('characters', 3560),
 ('character', 3514),
 ('don', 3468),
 ('man', 3460),
 ('show', 3432),
 ('watch', 3424),
 ('seen', 3414),
 ('then', 3358),
 ('little', 3341),
 ('still', 3340),
 ('make', 3303),
 ('could', 3237),
 ('never', 3226),
 ('being', 3217),
 ('where', 3173),
 ('does', 3069),
 ('over', 3017),
 ('any', 3002),
 ('while', 2899),
 ('know', 2833),
 ('did', 2790),
 ('years', 2758),
 ('here', 2740),
 ('ever', 2734),
 ('end', 2696),
 ('these', 2694),
 ('such', 2590),
 ('real', 2568),
 ('scene', 2567),
 ('back', 2547),
 ('those', 2485),
 ('though', 2475),
 ('off', 2463),
 ('new', 2458),
 ('your', 2453),
 ('go', 2440),
 ('acting', 2437),
 ('plot', 2432),
 ('world', 2429),
 ('scenes', 2427),
 ('say', 2414),
 ('through', 2409),
 ('makes', 2390),
 ('better', 2381),
 ('now', 2368),
 ('work', 2346),
 ('young', 2343),
 ('old', 2311),
 ('ve', 2307),
 ('find', 2272),
 ('both', 2248),
 ('before', 2177),
 ('us', 2162),
 ('again', 2158),
 ('series', 2153),
 ('quite', 2143),
 ('something', 2135),
 ('cast', 2133),
 ('should', 2121),
 ('part', 2098),
 ('always', 2088),
 ('lot', 2087),
 ('another', 2075),
 ('actors', 2047),
 ('director', 2040),
 ('family', 2032),
 ('between', 2016),
 ('own', 2016),
 ('m', 1998),
 ('may', 1997),
 ('same', 1972),
 ('role', 1967),
 ('watching', 1966),
 ('every', 1954),
 ('funny', 1953),
 ('doesn', 1935),
 ('performance', 1928),
 ('few', 1918),
 ('bad', 1907),
 ('look', 1900),
 ('re', 1884),
 ('why', 1855),
 ('things', 1849),
 ('times', 1832),
 ('big', 1815),
 ('however', 1795),
 ('actually', 1790),
 ('action', 1789),
 ('going', 1783),
 ('bit', 1757),
 ('comedy', 1742),
 ('down', 1740),
 ('music', 1738),
 ('must', 1728),
 ('take', 1709),
 ('saw', 1692),
 ('long', 1690),
 ('right', 1688),
 ('fun', 1686),
 ('fact', 1684),
 ('excellent', 1683),
 ('around', 1674),
 ('didn', 1672),
 ('without', 1671),
 ('thing', 1662),
 ('thought', 1639),
 ('got', 1635),
 ('each', 1630),
 ('day', 1614),
 ('feel', 1597),
 ('seems', 1596),
 ('come', 1594),
 ('done', 1586),
 ('beautiful', 1580),
 ('especially', 1572),
 ('played', 1571),
 ('almost', 1566),
 ('want', 1562),
 ('yet', 1556),
 ('give', 1553),
 ('pretty', 1549),
 ('last', 1543),
 ('since', 1519),
 ('different', 1504),
 ('although', 1501),
 ('gets', 1490),
 ('true', 1487),
 ('interesting', 1481),
 ('job', 1470),
 ('enough', 1455),
 ('our', 1454),
 ('shows', 1447),
 ('horror', 1441),
 ('woman', 1439),
 ('tv', 1400),
 ('probably', 1398),
 ('father', 1395),
 ('original', 1393),
 ('girl', 1390),
 ('point', 1379),
 ('plays', 1378),
 ('wonderful', 1372),
 ('course', 1358),
 ('far', 1358),
 ('john', 1350),
 ('rather', 1340),
 ('isn', 1328),
 ('ll', 1326),
 ('later', 1324),
 ('dvd', 1324),
 ('war', 1310),
 ('whole', 1310),
 ('d', 1307),
 ('away', 1306),
 ('found', 1306),
 ('screen', 1305),
 ('nothing', 1300),
 ('year', 1297),
 ('once', 1296),
 ('hard', 1294),
 ('together', 1280),
 ('am', 1277),
 ('set', 1277),
 ('having', 1266),
 ('making', 1265),
 ('place', 1263),
 ('comes', 1260),
 ('might', 1260),
 ('sure', 1253),
 ('american', 1248),
 ('play', 1245),
 ('kind', 1244),
 ('perfect', 1242),
 ('takes', 1242),
 ('performances', 1237),
 ('himself', 1230),
 ('everyone', 1221),
 ('worth', 1221),
 ('anyone', 1214),
 ('actor', 1203),
 ('three', 1201),
 ('wife', 1196),
 ('classic', 1192),
 ('goes', 1186),
 ('ending', 1178),
 ('version', 1168),
 ('star', 1149),
 ('enjoy', 1146),
 ('book', 1142),
 ('nice', 1132),
 ('everything', 1128),
 ('during', 1124),
 ('put', 1118),
 ('seeing', 1111),
 ('least', 1102),
 ('house', 1100),
 ('high', 1095),
 ('watched', 1094),
 ('loved', 1087),
 ('men', 1087),
 ('night', 1082),
 ('anything', 1075),
 ('guy', 1071),
 ('believe', 1071),
 ('top', 1063),
 ('amazing', 1058),
 ('hollywood', 1056),
 ('looking', 1053),
 ('main', 1044),
 ('definitely', 1043),
 ('gives', 1031),
 ('home', 1029),
 ('seem', 1028),
 ('episode', 1023),
 ('sense', 1020),
 ('audience', 1020),
 ('truly', 1017),
 ('special', 1011),
 ('fan', 1009),
 ('second', 1009),
 ('short', 1009),
 ('mind', 1005),
 ('human', 1001),
 ('recommend', 999),
 ('full', 996),
 ('black', 995),
 ('help', 991),
 ('along', 989),
 ('trying', 987),
 ('small', 986),
 ('death', 985),
 ('friends', 981),
 ('remember', 974),
 ('often', 970),
 ('said', 966),
 ('favorite', 962),
 ('heart', 959),
 ('early', 957),
 ('left', 956),
 ('until', 955),
 ('script', 954),
 ('let', 954),
 ('maybe', 937),
 ('today', 936),
 ('less', 934),
 ('live', 934),
 ('moments', 933),
 ('others', 929),
 ('brilliant', 926),
 ('shot', 925),
 ('liked', 923),
 ('become', 916),
 ('won', 915),
 ('used', 910),
 ('style', 907),
 ('mother', 895),
 ('lives', 894),
 ('came', 893),
 ('stars', 890),
 ('cinema', 889),
 ('looks', 885),
 ('perhaps', 884),
 ('read', 882),
 ('enjoyed', 879),
 ('boy', 875),
 ('drama', 873),
 ('highly', 871),
 ('given', 870),
 ('playing', 867),
 ('use', 864),
 ('next', 859),
 ('women', 858),
 ('fine', 857),
 ('effects', 856),
 ('kids', 854),
 ('entertaining', 853),
 ('need', 852),
 ('line', 850),
 ('works', 848),
 ('someone', 847),
 ('mr', 836),
 ('simply', 835),
 ('children', 833),
 ('picture', 833),
 ('face', 831),
 ('keep', 831),
 ('friend', 831),
 ('dark', 830),
 ('overall', 828),
 ('certainly', 828),
 ('minutes', 827),
 ('wasn', 824),
 ('history', 822),
 ('finally', 820),
 ('couple', 816),
 ('against', 815),
 ('son', 809),
 ('understand', 808),
 ('lost', 807),
 ('michael', 805),
 ('else', 801),
 ('throughout', 798),
 ('fans', 797),
 ('city', 792),
 ('reason', 789),
 ('written', 787),
 ('production', 787),
 ('several', 784),
 ('school', 783),
 ('rest', 781),
 ('based', 781),
 ('try', 780),
 ('dead', 776),
 ('hope', 775),
 ('strong', 768),
 ('white', 765),
 ('tell', 759),
 ('itself', 758),
 ('half', 753),
 ('person', 749),
 ('sometimes', 746),
 ('start', 744),
 ('past', 744),
 ('genre', 743),
 ('final', 739),
 ('beginning', 739),
 ('town', 738),
 ('art', 734),
 ('game', 732),
 ('humor', 732),
 ('idea', 731),
 ('yes', 731),
 ('late', 730),
 ('despite', 729),
 ('becomes', 729),
 ('case', 726),
 ('able', 726),
 ('money', 723),
 ('completely', 721),
 ('child', 721),
 ('side', 719),
 ('camera', 716),
 ('getting', 714),
 ('instead', 712),
 ('soon', 702),
 ('under', 700),
 ('viewer', 699),
 ('age', 697),
 ('days', 696),
 ('stories', 696),
 ('felt', 694),
 ('simple', 694),
 ('roles', 693),
 ('video', 688),
 ('either', 683),
 ('name', 683),
 ('doing', 677),
 ('turns', 674),
 ('wants', 671),
 ('close', 671),
 ('title', 669),
 ('wrong', 668),
 ('went', 666),
 ('james', 665),
 ('evil', 659),
 ('episodes', 657),
 ('budget', 657),
 ('relationship', 655),
 ('piece', 653),
 ('fantastic', 653),
 ('david', 651),
 ('turn', 648),
 ('murder', 646),
 ('parts', 645),
 ('brother', 644),
 ('head', 643),
 ('absolutely', 643),
 ('experience', 642),
 ('eyes', 641),
 ('sex', 638),
 ('direction', 637),
 ('called', 637),
 ('directed', 636),
 ('lines', 634),
 ('behind', 633),
 ('sort', 632),
 ('actress', 631),
 ('lead', 630),
 ('oscar', 628),
 ('example', 627),
 ('including', 627),
 ('known', 625),
 ('musical', 625),
 ('chance', 621),
 ('score', 620),
 ('hit', 619),
 ('feeling', 619),
 ('already', 619),
 ('voice', 615),
 ('living', 612),
 ('moment', 612),
 ('low', 610),
 ('supporting', 610),
 ('ago', 609),
 ('themselves', 608),
 ('hilarious', 605),
 ('reality', 605),
 ('jack', 604),
 ('told', 603),
 ('hand', 601),
 ('dialogue', 600),
 ('quality', 600),
 ('moving', 600),
 ('happy', 599),
 ('song', 599),
 ('matter', 598),
 ('paul', 598),
 ('light', 594),
 ('future', 593),
 ('entire', 592),
 ('finds', 591),
 ('gave', 589),
 ('laugh', 587),
 ('released', 586),
 ('expect', 584),
 ('fight', 581),
 ('particularly', 580),
 ('cinematography', 579),
 ('police', 579),
 ('sound', 578),
 ('whose', 578),
 ('type', 578),
 ('enjoyable', 573),
 ('view', 573),
 ('number', 572),
 ('husband', 572),
 ('daughter', 572),
 ('romantic', 572),
 ('documentary', 571),
 ('self', 570),
 ('took', 569),
 ('superb', 569),
 ('modern', 569),
 ('robert', 569),
 ('mean', 566),
 ('shown', 563),
 ('coming', 561),
 ('important', 560),
 ('king', 559),
 ('leave', 559),
 ('change', 558),
 ('somewhat', 555),
 ('wanted', 555),
 ('tells', 554),
 ('events', 552),
 ('country', 552),
 ('run', 552),
 ('career', 552),
 ('heard', 550),
 ('season', 550),
 ('girls', 549),
 ('greatest', 549),
 ('etc', 547),
 ('care', 546),
 ('starts', 545),
 ('english', 542),
 ('killer', 541),
 ('tale', 540),
 ('totally', 540),
 ('animation', 540),
 ('guys', 540),
 ('usual', 539),
 ('opinion', 535),
 ('miss', 535),
 ('violence', 531),
 ('easy', 531),
 ('songs', 530),
 ('british', 528),
 ('says', 526),
 ('realistic', 525),
 ('writing', 524),
 ('writer', 522),
 ('act', 522),
 ('comic', 521),
 ('thriller', 519),
 ('television', 517),
 ('power', 516),
 ('ones', 515),
 ('kid', 514),
 ('novel', 513),
 ('york', 513),
 ('alone', 512),
 ('problem', 512),
 ('attention', 509),
 ('involved', 508),
 ('extremely', 507),
 ('kill', 507),
 ('seemed', 506),
 ('hero', 505),
 ('french', 505),
 ('rock', 504),
 ('stuff', 501),
 ('wish', 499),
 ('begins', 498),
 ('sad', 497),
 ('taken', 497),
 ('ways', 496),
 ('richard', 495),
 ('knows', 494),
 ('atmosphere', 493),
 ('similar', 491),
 ('car', 491),
 ('surprised', 491),
 ('taking', 491),
 ('perfectly', 490),
 ('george', 490),
 ('team', 489),
 ('across', 489),
 ('eye', 489),
 ('sequence', 489),
 ('due', 488),
 ('serious', 488),
 ('powerful', 488),
 ('room', 488),
 ('among', 488),
 ('order', 487),
 ('cannot', 487),
 ('strange', 487),
 ('b', 487),
 ('beauty', 486),
 ('famous', 485),
 ('tries', 484),
 ('herself', 484),
 ('happened', 484),
 ('myself', 484),
 ('class', 483),
 ('four', 482),
 ('cool', 481),
 ('theme', 479),
 ('release', 479),
 ('anyway', 479),
 ('opening', 478),
 ('entertainment', 477),
 ('slow', 475),
 ('ends', 475),
 ('exactly', 475),
 ('unique', 475),
 ('o', 474),
 ('level', 474),
 ('easily', 474),
 ('red', 474),
 ('interest', 472),
 ('happen', 471),
 ('crime', 470),
 ('viewing', 468),
 ('sets', 467),
 ('memorable', 467),
 ('stop', 466),
 ('group', 466),
 ('dance', 463),
 ('sister', 463),
 ('working', 463),
 ('problems', 463),
 ('message', 463),
 ('knew', 462),
 ('mystery', 461),
 ('nature', 461),
 ('bring', 460),
 ('thinking', 459),
 ('brought', 459),
 ('believable', 459),
 ('mostly', 458),
 ('disney', 457),
 ('couldn', 457),
 ('society', 456),
 ('within', 455),
 ('lady', 455),
 ('blood', 454),
 ('upon', 453),
 ('parents', 453),
 ('viewers', 453),
 ('soundtrack', 452),
 ('form', 452),
 ('usually', 452),
 ('peter', 452),
 ('tom', 452),
 ('meets', 452),
 ('local', 450),
 ('follow', 448),
 ('certain', 448),
 ('whether', 447),
 ('possible', 446),
 ('emotional', 445),
 ('de', 444),
 ('killed', 444),
 ('above', 444),
 ('god', 443),
 ('middle', 443),
 ('happens', 442),
 ('flick', 442),
 ('needs', 442),
 ('masterpiece', 441),
 ('period', 440),
 ('major', 440),
 ('haven', 439),
 ('named', 439),
 ('particular', 438),
 ('th', 438),
 ('earth', 437),
 ('feature', 437),
 ('stand', 436),
 ('words', 435),
 ('typical', 435),
 ('elements', 433),
 ('obviously', 433),
 ('romance', 431),
 ('jane', 430),
 ('showing', 427),
 ('yourself', 427),
 ('brings', 426),
 ('fantasy', 426),
 ('america', 423),
 ('guess', 423),
 ('unfortunately', 422),
 ('huge', 422),
 ('running', 421),
 ('indeed', 421),
 ('talent', 420),
 ('stage', 419),
 ('started', 418),
 ('japanese', 417),
 ('leads', 417),
 ('sweet', 417),
 ('poor', 416),
 ('deal', 416),
 ('personal', 413),
 ('incredible', 413),
 ('fast', 412),
 ('became', 410),
 ('deep', 410),
 ('hours', 409),
 ('giving', 408),
 ('nearly', 408),
 ('dream', 408),
 ('turned', 407),
 ('clearly', 407),
 ('near', 406),
 ('obvious', 406),
 ('cut', 405),
 ('surprise', 405),
 ('era', 404),
 ('body', 404),
 ('hour', 403),
 ('five', 403),
 ('female', 403),
 ('note', 399),
 ('truth', 398),
 ('learn', 398),
 ('tony', 397),
 ('feels', 397),
 ('except', 397),
 ('match', 397),
 ('complete', 394),
 ('clear', 394),
 ('filmed', 394),
 ('lots', 393),
 ('eventually', 393),
 ('street', 393),
 ('keeps', 393),
 ('older', 393),
 ('buy', 392),
 ('william', 391),
 ('stewart', 391),
 ('fall', 390),
 ('joe', 390),
 ('meet', 390),
 ('rating', 389),
 ('unlike', 389),
 ('talking', 389),
 ('shots', 389),
 ('difficult', 389),
 ('means', 388),
 ('dramatic', 388),
 ('present', 386),
 ('appears', 386),
 ('situation', 386),
 ('subject', 386),
 ('wonder', 386),
 ('comments', 385),
 ('sequences', 383),
 ('general', 383),
 ('lee', 383),
 ('earlier', 382),
 ('points', 382),
 ('check', 379),
 ('gone', 379),
 ('ten', 378),
 ('suspense', 378),
 ('recommended', 378),
 ('business', 377),
 ('third', 377),
 ('beyond', 375),
 ('leaves', 375),
 ('talk', 375),
 ('portrayal', 374),
 ('beautifully', 373),
 ('bill', 372),
 ('single', 372),
 ('word', 371),
 ('plenty', 371),
 ('whom', 370),
 ('falls', 370),
 ('battle', 369),
 ('non', 369),
 ('scary', 369),
 ('figure', 369),
 ('using', 368),
 ('return', 368),
 ('add', 367),
 ('doubt', 367),
 ('success', 366),
 ('hear', 366),
 ('solid', 366),
 ('political', 365),
 ('oh', 365),
 ('touching', 365),
 ('jokes', 365),
 ('awesome', 364),
 ('boys', 364),
 ('hell', 364),
 ('sexual', 362),
 ('dog', 362),
 ('recently', 362),
 ('please', 361),
 ('wouldn', 361),
 ('features', 361),
 ('straight', 361),
 ('forget', 360),
 ('setting', 360),
 ('lack', 360),
 ('married', 359),
 ('mark', 359),
 ('social', 357),
 ('adventure', 356),
 ('interested', 356),
 ('terrific', 355),
 ('brothers', 355),
 ('sees', 355),
 ('actual', 355),
 ('move', 354),
 ('call', 354),
 ('dr', 353),
 ('theater', 353),
 ('various', 353),
 ('animated', 352),
 ('western', 351),
 ('baby', 350),
 ('space', 350),
 ('leading', 348),
 ('disappointed', 348),
 ('portrayed', 346),
 ('aren', 346),
 ('screenplay', 345),
 ('smith', 345),
 ('hate', 344),
 ('towards', 344),
 ('noir', 343),
 ('outstanding', 342),
 ('kelly', 342),
 ('decent', 342),
 ('directors', 341),
 ('journey', 341),
 ('none', 340),
 ('effective', 340),
 ('looked', 340),
 ('fi', 339),
 ('storyline', 339),
 ('mary', 339),
 ('cold', 339),
 ('caught', 339),
 ('sci', 339),
 ('rich', 338),
 ('charming', 338),
 ('harry', 337),
 ('manages', 337),
 ('popular', 337),
 ('rare', 337),
 ('spirit', 336),
 ('appreciate', 335),
 ('open', 335),
 ('basically', 334),
 ('acted', 334),
 ('moves', 334),
 ('inside', 333),
 ('deserves', 333),
 ('subtle', 333),
 ('mention', 333),
 ('boring', 333),
 ('pace', 333),
 ('century', 333),
 ('background', 332),
 ('familiar', 332),
 ('ben', 331),
 ('supposed', 330),
 ('creepy', 330),
 ('secret', 329),
 ('jim', 328),
 ('die', 328),
 ('effect', 327),
 ('natural', 327),
 ('question', 327),
 ('impressive', 326),
 ('language', 326),
 ('rate', 326),
 ('intelligent', 325),
 ('saying', 325),
 ('telling', 324),
 ('material', 324),
 ('realize', 324),
 ('scott', 324),
 ('singing', 323),
 ('dancing', 322),
 ('adult', 321),
 ('visual', 321),
 ('imagine', 321),
 ('office', 320),
 ('kept', 320),
 ('uses', 319),
 ('wait', 318),
 ('pure', 318),
 ('stunning', 318),
 ('previous', 317),
 ('seriously', 317),
 ('copy', 317),
 ('review', 317),
 ('hot', 316),
 ('magic', 316),
 ('reading', 316),
 ('create', 316),
 ('created', 316),
 ('somehow', 316),
 ('air', 315),
 ('escape', 315),
 ('crazy', 315),
 ('attempt', 315),
 ('stay', 315),
 ('frank', 315),
 ('hands', 314),
 ('filled', 313),
 ('surprisingly', 312),
 ('average', 312),
 ('expected', 312),
 ('complex', 311),
 ('studio', 310),
 ('successful', 310),
 ('quickly', 310),
 ('plus', 309),
 ('male', 309),
 ('co', 307),
 ('exciting', 306),
 ('following', 306),
 ('images', 306),
 ('minute', 306),
 ('casting', 306),
 ('themes', 305),
 ('members', 305),
 ('follows', 305),
 ('e', 305),
 ('reasons', 305),
 ('german', 305),
 ('touch', 304),
 ('edge', 304),
 ('cute', 304),
 ('genius', 304),
 ('free', 304),
 ('outside', 303),
 ('ok', 302),
 ('younger', 302),
 ('reviews', 302),
 ('admit', 302),
 ('odd', 301),
 ('master', 301),
 ('fighting', 301),
 ('break', 300),
 ('comment', 300),
 ('thanks', 300),
 ('recent', 300),
 ('apart', 299),
 ('begin', 298),
 ('emotions', 298),
 ('lovely', 298),
 ('doctor', 297),
 ('party', 297),
 ('italian', 297),
 ('sequel', 296),
 ('clever', 296),
 ...]

In [10]:

    

pos_neg_ratios = Counter()

for term,cnt in list(total_counts.most_common()):
    if(cnt > 100):
        pos_neg_ratio = positive_counts[term] / float(negative_counts[term]+1)
        pos_neg_ratios[term] = pos_neg_ratio

for word,ratio in pos_neg_ratios.most_common():
    if(ratio > 1):
        pos_neg_ratios[word] = np.log(ratio)
    else:
        pos_neg_ratios[word] = -np.log((1 / (ratio+0.01)))


    

In [11]:

    

# words most frequently seen in a review with a "POSITIVE" label
pos_neg_ratios.most_common()


    

    
Out[11]:





[('edie', 4.6913478822291435),
 ('paulie', 4.0775374439057197),
 ('felix', 3.1527360223636558),
 ('polanski', 2.8233610476132043),
 ('matthau', 2.8067217286092401),
 ('victoria', 2.6810215287142909),
 ('mildred', 2.6026896854443837),
 ('gandhi', 2.5389738710582761),
 ('flawless', 2.451005098112319),
 ('superbly', 2.2600254785752498),
 ('perfection', 2.1594842493533721),
 ('astaire', 2.1400661634962708),
 ('captures', 2.0386195471595809),
 ('voight', 2.0301704926730531),
 ('wonderfully', 2.0218960560332353),
 ('powell', 1.9783454248084671),
 ('brosnan', 1.9547990964725592),
 ('lily', 1.9203768470501485),
 ('bakshi', 1.9029851043382795),
 ('lincoln', 1.9014583864844796),
 ('refreshing', 1.8551812956655511),
 ('breathtaking', 1.8481124057791867),
 ('bourne', 1.8478489358790986),
 ('lemmon', 1.8458266904983307),
 ('delightful', 1.8002701588959635),
 ('flynn', 1.7996646487351682),
 ('andrews', 1.7764919970972666),
 ('homer', 1.7692866133759964),
 ('beautifully', 1.7626953362841438),
 ('soccer', 1.7578579175523736),
 ('elvira', 1.7397031072720019),
 ('underrated', 1.7197859696029656),
 ('gripping', 1.7165360479904674),
 ('superb', 1.7091514458966952),
 ('delight', 1.6714733033535532),
 ('welles', 1.6677068205580761),
 ('sadness', 1.663505133704376),
 ('sinatra', 1.6389967146756448),
 ('touching', 1.637217476541176),
 ('timeless', 1.62924053973028),
 ('macy', 1.6211339521972916),
 ('unforgettable', 1.6177367152487956),
 ('favorites', 1.6158688027643908),
 ('stewart', 1.6119987332957739),
 ('extraordinary', 1.6094379124341003),
 ('hartley', 1.6094379124341003),
 ('sullivan', 1.6094379124341003),
 ('brilliantly', 1.5950491749820008),
 ('friendship', 1.5677652160335325),
 ('wonderful', 1.5645425925262093),
 ('palma', 1.5553706911638245),
 ('magnificent', 1.54663701119507),
 ('finest', 1.5462590108125689),
 ('jackie', 1.5439233053234738),
 ('ritter', 1.5404450409471491),
 ('tremendous', 1.5184661342283736),
 ('freedom', 1.5091151908062312),
 ('fantastic', 1.5048433868558566),
 ('terrific', 1.5026699370083942),
 ('sidney', 1.493925025312256),
 ('noir', 1.493925025312256),
 ('outstanding', 1.4910053152089213),
 ('mann', 1.4894785973551214),
 ('pleasantly', 1.4894785973551214),
 ('nancy', 1.488077055429833),
 ('marie', 1.4825711915553104),
 ('marvelous', 1.4739999415389962),
 ('excellent', 1.4647538505723599),
 ('ruth', 1.4596256342054401),
 ('stanwyck', 1.4412101187160054),
 ('widmark', 1.4350845252893227),
 ('splendid', 1.4271163556401458),
 ('chan', 1.423108334242607),
 ('exceptional', 1.4201959127955721),
 ('tender', 1.410986973710262),
 ('gentle', 1.4078005663408544),
 ('poignant', 1.4022947024663317),
 ('gem', 1.3932148039644643),
 ('amazing', 1.3919815802404802),
 ('fisher', 1.3862943611198906),
 ('captivating', 1.3862943611198906),
 ('chilling', 1.3862943611198906),
 ('davies', 1.3862943611198906),
 ('darker', 1.3652409519220583),
 ('april', 1.3499267169490159),
 ('kelly', 1.3461743673304654),
 ('blake', 1.3418425985490567),
 ('overlooked', 1.329135947279942),
 ('ralph', 1.32818673031261),
 ('bette', 1.3156767939059373),
 ('hoffman', 1.3150668518315229),
 ('cole', 1.3121863889661687),
 ('shines', 1.3049487216659381),
 ('powerful', 1.2999662776313934),
 ('notch', 1.2950456896547455),
 ('remarkable', 1.2883688239495823),
 ('pitt', 1.286210902562908),
 ('winters', 1.2833463918674481),
 ('vivid', 1.2762934659055623),
 ('gritty', 1.2757524867200667),
 ('giallo', 1.2745029551317739),
 ('portrait', 1.2704625455947689),
 ('innocence', 1.2694300209805796),
 ('psychiatrist', 1.2685113254635072),
 ('favorite', 1.2668956297860055),
 ('ensemble', 1.2656663733312759),
 ('stunning', 1.2622417124499117),
 ('burns', 1.259880436264232),
 ('garbo', 1.258954938743289),
 ('barbara', 1.2580400255962119),
 ('philip', 1.2527629684953681),
 ('panic', 1.2527629684953681),
 ('holly', 1.2527629684953681),
 ('carol', 1.2481440226390734),
 ('perfect', 1.246742480713785),
 ('appreciated', 1.2462482874741743),
 ('favourite', 1.2411123512753928),
 ('journey', 1.2367626271489269),
 ('rural', 1.235471471385307),
 ('bond', 1.2321436812926323),
 ('builds', 1.2305398317106577),
 ('brilliant', 1.2287554137664785),
 ('brooklyn', 1.2286654169163074),
 ('von', 1.225175011976539),
 ('recommended', 1.2163953243244932),
 ('unfolds', 1.2163953243244932),
 ('daniel', 1.20215296760895),
 ('perfectly', 1.1971931173405572),
 ('crafted', 1.1962507582320256),
 ('prince', 1.1939224684724346),
 ('troubled', 1.192138346678933),
 ('consequences', 1.1865810616140668),
 ('haunting', 1.1814999484738773),
 ('cinderella', 1.180052620608284),
 ('alexander', 1.1759989522835299),
 ('emotions', 1.1753049094563641),
 ('boxing', 1.1735135968412274),
 ('subtle', 1.1734135017508081),
 ('curtis', 1.1649873576129823),
 ('rare', 1.1566438362402944),
 ('loved', 1.1563661500586044),
 ('daughters', 1.1526795099383853),
 ('courage', 1.1438688802562305),
 ('dentist', 1.1426722784621401),
 ('highly', 1.1420208631618658),
 ('nominated', 1.1409146683587992),
 ('tony', 1.1397491942285991),
 ('draws', 1.1325138403437911),
 ('everyday', 1.1306150197542835),
 ('contrast', 1.1284652518177909),
 ('cried', 1.1213405397456659),
 ('fabulous', 1.1210851445201684),
 ('fay', 1.120591195386885),
 ('ned', 1.120591195386885),
 ('emma', 1.1184149159642893),
 ('sensitive', 1.113318436057805),
 ('smooth', 1.1089750757036563),
 ('dramas', 1.1080910326226534),
 ('today', 1.1050431789984001),
 ('helps', 1.1023091505494358),
 ('inspiring', 1.0986122886681098),
 ('jimmy', 1.0937696641923216),
 ('awesome', 1.0931328229034842),
 ('unique', 1.0881409888008142),
 ('tragic', 1.0871835928444868),
 ('intense', 1.0870514662670339),
 ('stellar', 1.0857088838322018),
 ('rival', 1.0822184788924332),
 ('provides', 1.0797081340289569),
 ('depression', 1.0782034170369026),
 ('shy', 1.0775588794702773),
 ('carrie', 1.076139432816051),
 ('blend', 1.0753554265038423),
 ('hank', 1.0736109864626924),
 ('diana', 1.0726368022648489),
 ('adorable', 1.0726368022648489),
 ('unexpected', 1.0722255334949147),
 ('achievement', 1.0668635903535293),
 ('bettie', 1.0663514264498881),
 ('happiness', 1.0632729222228008),
 ('glorious', 1.0608719606852626),
 ('davis', 1.0541605260972757),
 ('terrifying', 1.0525211814678428),
 ('beauty', 1.050410186850232),
 ('ideal', 1.0479685558493548),
 ('fears', 1.0467872208035236),
 ('hong', 1.0438040521731147),
 ('seasons', 1.0433496099930604),
 ('fascinating', 1.0414538748281612),
 ('carries', 1.0345904299031787),
 ('satisfying', 1.0321225473992768),
 ('definite', 1.0319209141694374),
 ('touched', 1.0296194171811581),
 ('greatest', 1.0248947127715422),
 ('creates', 1.0241097613701886),
 ('aunt', 1.023388867430522),
 ('walter', 1.022328983918479),
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 ('portrayal', 1.0189810189761024),
 ('ann', 1.0127808528183286),
 ('enterprise', 1.0116009116784799),
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 ('navy', 0.99493385919326827),
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 ('holmes', 0.97246100334120955),
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 ('vhs', 0.97116734209998934),
 ('eerie', 0.97116734209998934),
 ('grim', 0.96873720724669754),
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 ('danny', 0.93967118051821874),
 ('anime', 0.93378388932167222),
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 ('mysteries', 0.92953595862417571),
 ('hal', 0.92953595862417571),
 ('louis', 0.92871325187271225),
 ('charming', 0.92520609553210742),
 ('urban', 0.92367083917177761),
 ('allows', 0.92183091224977043),
 ('impact', 0.91815814604895041),
 ('lifestyle', 0.91629073187415511),
 ('italy', 0.91629073187415511),
 ('gradually', 0.91629073187415511),
 ('spy', 0.91289514287301687),
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 ('reveals', 0.90490540964902977),
 ('raw', 0.90445627422715225),
 ('funniest', 0.90078654533818991),
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 ('essential', 0.87373111745535925),
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 ('fonda', 0.87294029100054127),
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 ('searching', 0.4393666597838457),
 ('rap', 0.43891304217570443),
 ('light', 0.43884433018199892),
 ('elizabeth', 0.43872232986464682),
 ('marry', 0.43861731542506488),
 ('controversial', 0.43825493093115531),
 ('learned', 0.43825493093115531),
 ('oz', 0.43825493093115531),
 ('slowly', 0.43785660389939979),
 ('bridge', 0.43721380642274466),
 ('wayne', 0.43721380642274466),
 ('comedic', 0.43721380642274466),
 ('thrilling', 0.43721380642274466),
 ('married', 0.43658501682196887),
 ('nazi', 0.4361020775700542),
 ('murder', 0.4353180712578455),
 ('physical', 0.4353180712578455),
 ('johnny', 0.43483971678806865),
 ('michelle', 0.43445264498141672),
 ('wallace', 0.43403848055222038),
 ('comedies', 0.43395706390247063),
 ('silent', 0.43395706390247063),
 ('played', 0.43387244114515305),
 ('international', 0.43363598507486073),
 ('vision', 0.43286408229627887),
 ('intelligent', 0.43196704885367099),
 ('shop', 0.43078291609245434),
 ('also', 0.43036720209769169),
 ('levels', 0.4302451371066513),
 ('miss', 0.43006426712153217),
 ('ocean', 0.4295626596872249),
 ...]

In [12]:

    

# words most frequently seen in a review with a "NEGATIVE" label
list(reversed(pos_neg_ratios.most_common()))[0:30]


    

    
Out[12]:





[('boll', -4.0778152602708904),
 ('uwe', -3.9218753018711578),
 ('seagal', -3.3202501058581921),
 ('unwatchable', -3.0269848170580955),
 ('stinker', -2.9876839403711624),
 ('mst', -2.7753833211707968),
 ('incoherent', -2.7641396677532537),
 ('unfunny', -2.5545257844967644),
 ('waste', -2.4907515123361046),
 ('blah', -2.4475792789485005),
 ('horrid', -2.3715779644809971),
 ('pointless', -2.3451073877136341),
 ('atrocious', -2.3187369339642556),
 ('redeeming', -2.2667790015910296),
 ('prom', -2.2601040980178784),
 ('drivel', -2.2476029585766928),
 ('lousy', -2.2118080125207054),
 ('worst', -2.1930856334332267),
 ('laughable', -2.172468615469592),
 ('awful', -2.1385076866397488),
 ('poorly', -2.1326133844207011),
 ('wasting', -2.1178155545614512),
 ('remotely', -2.111046881095167),
 ('existent', -2.0024805005437076),
 ('boredom', -1.9241486572738005),
 ('miserably', -1.9216610938019989),
 ('sucks', -1.9166645809588516),
 ('uninspired', -1.9131499212248517),
 ('lame', -1.9117232884159072),
 ('insult', -1.9085323769376259)]

In [13]:

    

from IPython.display import Image

review = "This was a horrible, terrible movie."

Image(filename='sentiment_network.png')


    

    
Out[13]:

In [14]:

    

review = "The movie was excellent"

Image(filename='sentiment_network_pos.png')


    

    
Out[14]:

In [15]:

    

vocab = set(total_counts.keys())
vocab_size = len(vocab)
print(vocab_size)


    

    




74074

In [16]:

    

list(vocab)


    

    
Out[16]:





['',
 'sarat',
 'augers',
 'automation',
 'duller',
 'hippo',
 'isolated',
 'vanquishing',
 'lorraina',
 'hat',
 'merkel',
 'papal',
 'visionary',
 'broadhurst',
 'czar',
 'trespasses',
 'ok',
 'inventive',
 'freire',
 'bens',
 'jimnez',
 'dissapointment',
 'hasidic',
 'hellenic',
 'cussed',
 'carstone',
 'recast',
 'sympathizing',
 'clenteen',
 'otac',
 'puzzlers',
 'explosively',
 'nutcases',
 'byways',
 'kurupt',
 'rating',
 'glaser',
 'tykwer',
 'vancleef',
 'complaisance',
 'mazes',
 'incensed',
 'distant',
 'infancy',
 'rookie',
 'utters',
 'cubensis',
 'behemoth',
 'bogs',
 'tare',
 'pita',
 'invites',
 'heyijustleftmycoatbehind',
 'caterer',
 'attempting',
 'armateur',
 'bedi',
 'stoney',
 'unscrupulously',
 'intermediate',
 'mcinnerny',
 'psychotronic',
 'undefinable',
 'beery',
 'pathetic',
 'malozzie',
 'rob',
 'skinamax',
 'donahue',
 'backorder',
 'psychology',
 'passed',
 'enunciate',
 'plays',
 'contributers',
 'fears',
 'englebert',
 'instinctual',
 'elainor',
 'wounded',
 'bumper',
 'pacingly',
 'africa',
 'moderately',
 'pantry',
 'odysseys',
 'flubbed',
 'titlesgreat',
 'pooja',
 'sienna',
 'kont',
 'castulo',
 'expecting',
 'artifices',
 'twitch',
 'disquiet',
 'rochester',
 'queuing',
 'avowedly',
 'botcher',
 'maille',
 'likens',
 'manuals',
 'unidiomatic',
 'outdoes',
 'overachieving',
 'animaux',
 'boobytraps',
 'slow',
 'blanzee',
 'elitist',
 'okada',
 'shun',
 'spain',
 'mmmmmmm',
 'beautifull',
 'scurrilous',
 'artisty',
 'lynching',
 'heffer',
 'zeons',
 'cheer',
 'demonstrably',
 'monstrously',
 'jolene',
 'springerland',
 'grants',
 'boggles',
 'unpublished',
 'glares',
 'thang',
 'rostov',
 'treasured',
 'moustaches',
 'myerson',
 'fi',
 'untrue',
 'tautou',
 'tantamount',
 'mckenzies',
 'execute',
 'shalom',
 'lakhan',
 'squashy',
 'gogh',
 'fiasco',
 'hoodoo',
 'whats',
 'fatuous',
 'ambushed',
 'cartooned',
 'diff',
 'blackbuster',
 'grottos',
 'zorba',
 'chutney',
 'dena',
 'paycheque',
 'elation',
 'morphine',
 'genitals',
 'jabba',
 'witte',
 'nervously',
 'overloud',
 'katha',
 'mei',
 'dislikeable',
 'unity',
 'spurted',
 'garand',
 'bashful',
 'hobble',
 'moog',
 'fend',
 'lodoss',
 'timelessness',
 'ton',
 'puny',
 'cursing',
 'hjelmet',
 'widely',
 'macluhen',
 'deadwood',
 'quakers',
 'piglet',
 'rimmed',
 'impending',
 'penquin',
 'touting',
 'accentuates',
 'tour',
 'deplorable',
 'sprezzatura',
 'btchiness',
 'miaows',
 'ngyuen',
 'mitowa',
 'thinning',
 'between',
 'landscaper',
 'sperms',
 'casa',
 'strawberries',
 'dimaggio',
 'venger',
 'disadvantageous',
 'tuba',
 'flavin',
 'contended',
 'skerritt',
 'bendy',
 'governors',
 'width',
 'acting',
 'prosthetic',
 'goodfascinating',
 'pastries',
 'stoker',
 'nella',
 'loooooooove',
 'bitched',
 'delbert',
 'drivin',
 'valuing',
 'rover',
 'confluences',
 'beeps',
 'cack',
 'saurabh',
 'guignol',
 'littered',
 'groupies',
 'amrutlal',
 'inch',
 'collisions',
 'abs',
 'spurting',
 'nudist',
 'fifteen',
 'consistently',
 'intonations',
 'moria',
 'bloodied',
 'yamada',
 'semi',
 'opprobrium',
 'tearjerkers',
 'stickler',
 'dispised',
 'sweatdroid',
 'generic',
 'else',
 'eaten',
 'predecessors',
 'whimsically',
 'gloss',
 'instructions',
 'concorde',
 'rashly',
 'capitalists',
 'grove',
 'fawcett',
 'blabber',
 'stratton',
 'burkes',
 'bruisingly',
 'railsback',
 'libertini',
 'patrol',
 'nasal',
 'wireframe',
 'deliberately',
 'shootem',
 'sceenplay',
 'earliest',
 'sidious',
 'title',
 'ethereally',
 'cawley',
 'squashing',
 'obstructs',
 'hounded',
 'vigor',
 'expands',
 'pronouncements',
 'darkish',
 'hatchet',
 'errol',
 'empress',
 'efrem',
 'clu',
 'apposed',
 'serbedzija',
 'calvero',
 'clementine',
 'inaccessible',
 'stiff',
 'brush',
 'succulent',
 'pharaoh',
 'demer',
 'empathic',
 'semen',
 'antiwar',
 'wroting',
 'squaw',
 'discouragement',
 'mcintire',
 'motivating',
 'muy',
 'reunions',
 'possessiveness',
 'victorian',
 'mya',
 'storia',
 'running',
 'backstory',
 'aphrodisiac',
 'steadiness',
 'empirical',
 'behemoths',
 'ubermensch',
 'sucky',
 'qaida',
 'demolitions',
 'cesare',
 'necron',
 'allegory',
 'leeves',
 'privateer',
 'bhat',
 'dancers',
 'crookedness',
 'giss',
 'pitbull',
 'required',
 'boyish',
 'absorbs',
 'taupin',
 'fudged',
 'surfboards',
 'wwwwwwwaaaaaaaaaaaayyyyyyyyyyy',
 'hammeresses',
 'responsive',
 'biographer',
 'teta',
 'freely',
 'billionare',
 'sebastiaans',
 'tendency',
 'telecommunications',
 'inseparability',
 'wifey',
 'oeils',
 'kargil',
 'ganay',
 'mcparland',
 'carnal',
 'slapsticks',
 'culver',
 'prosecute',
 'rychard',
 'ginny',
 'creaters',
 'break',
 'boob',
 'sneha',
 'tuttle',
 'roadway',
 'segregation',
 'ululating',
 'bum',
 'protestants',
 'fictionalized',
 'seann',
 'blesses',
 'buzby',
 'microfilmed',
 'fascist',
 'panhandler',
 'mushy',
 'midseason',
 'clubbers',
 'tohma',
 'ambrose',
 'proustian',
 'excretable',
 'countdown',
 'incorrectness',
 'intoxication',
 'chacha',
 'ziv',
 'marta',
 'chartreuse',
 'terminate',
 'dredd',
 'comfortably',
 'theoscarsblog',
 'sarcasms',
 'colossus',
 'emsworth',
 'privileges',
 'wallece',
 'piercing',
 'casars',
 'pharma',
 'mannequins',
 'hindrances',
 'cockamamie',
 'videos',
 'dinosuars',
 'intrigues',
 'menen',
 'hijackers',
 'strumpet',
 'doinel',
 'imaginatively',
 'railbird',
 'variance',
 'incongruity',
 'analog',
 'meningitis',
 'lube',
 'henchman',
 'stripteases',
 'lasts',
 'assasain',
 'blacker',
 'appologise',
 'dinosaurus',
 'oldtimer',
 'yorker',
 'wachtang',
 'craydon',
 'universe',
 'loulla',
 'xv',
 'spanked',
 'polchak',
 'cor',
 'unconstructive',
 'personnal',
 'ion',
 'ruthie',
 'hata',
 'colonised',
 'bataan',
 'yasbeck',
 'revolution',
 'looooooong',
 'preview',
 'melachonic',
 'cheerless',
 'galatica',
 'tow',
 'stagy',
 'eisen',
 'smorgasbord',
 'fumbling',
 'marvelously',
 'recriminations',
 'ballyhooed',
 'chineese',
 'hitters',
 'heartened',
 'condor',
 'send',
 'anesthesia',
 'greist',
 'dirtier',
 'mellifluous',
 'restrains',
 'awsomeness',
 'heston',
 'psmith',
 'warningi',
 'perplexedly',
 'dismissive',
 'totem',
 'elysee',
 'outdid',
 'trilateralists',
 'helmut',
 'appy',
 'irritatingly',
 'predict',
 'decker',
 'chins',
 'lowsy',
 'prospects',
 'borg',
 'protocols',
 'anatomising',
 'credibly',
 'misawa',
 'centering',
 'merrill',
 'naps',
 'tableware',
 'crimedies',
 'grip',
 'donations',
 'naudets',
 'allotting',
 'depressurization',
 'romagna',
 'invalidity',
 'westernized',
 'nolin',
 'tasted',
 'pilippinos',
 'presumbably',
 'watered',
 'tremulous',
 'derangement',
 'reburn',
 'creature',
 'uniquely',
 'hollandish',
 'dejas',
 'mover',
 'categorizing',
 'cartwheel',
 'expires',
 'yank',
 'loreno',
 'squeaks',
 'brows',
 'creaks',
 'dissects',
 'summation',
 'cinematography',
 'droves',
 'emphasizes',
 'coordinator',
 'filmaking',
 'stubborn',
 'foggiest',
 'unhappily',
 'neanderthal',
 'odbray',
 'consumptive',
 'celestine',
 'chad',
 'brim',
 'dr',
 'loopy',
 'dope',
 'xlr',
 'kibbutz',
 'witness',
 'compelled',
 'dolphin',
 'sleepapedic',
 'akhras',
 'reuniting',
 'bosworth',
 'ywca',
 'motto',
 'rivers',
 'erbe',
 'swingin',
 'yawn',
 'hikers',
 'kielberg',
 'puzzler',
 'purses',
 'melodramatic',
 'tenet',
 'realty',
 'misinterpretations',
 'sunnygate',
 'immaturity',
 'versace',
 'heiki',
 'cowper',
 'gitmo',
 'eponymous',
 'theyd',
 'schygula',
 'shuttered',
 'hopeing',
 'treasurer',
 'tock',
 'kaif',
 'makavejev',
 'underestimates',
 'canoes',
 'skywarriors',
 'matronly',
 'nigel',
 'victimizer',
 'coffees',
 'scholes',
 'sara',
 'epic',
 'stingers',
 'ivanhoe',
 'shallot',
 'avid',
 'jir',
 'pfeh',
 'craft',
 'mansions',
 'joab',
 'squared',
 'insufferably',
 'hestons',
 'steels',
 'organised',
 'homages',
 'imports',
 'defilers',
 'spock',
 'adaptation',
 'the',
 'facile',
 'orca',
 'tebaldi',
 'yarn',
 'dushku',
 'edwrad',
 'denouncing',
 'rivalries',
 'decaffeinated',
 'defininitive',
 'disciples',
 'bondi',
 'fellas',
 'cope',
 'connie',
 'hisako',
 'yamaguchi',
 'introduction',
 'celebi',
 'bolting',
 'angeletti',
 'appointment',
 'coincide',
 'overdramaticizing',
 'blossomed',
 'egghead',
 'newell',
 'amitji',
 'excommunication',
 'persecutions',
 'sniping',
 'antonis',
 'committees',
 'savvy',
 'commercially',
 'popinjays',
 'barbarism',
 'sidelined',
 'christopherson',
 'toast',
 'interests',
 'congruent',
 'baroness',
 'dwayne',
 'maneuverability',
 'larger',
 'insuring',
 'impoverished',
 'revisionist',
 'stylophone',
 'alahani',
 'zeroes',
 'boreanaz',
 'edwedge',
 'cairo',
 'flintstone',
 'clenching',
 'groping',
 'cushionantonietta',
 'haig',
 'dumblaine',
 'architect',
 'sharpening',
 'kay',
 'rdb',
 'perceptive',
 'beefcake',
 'samedi',
 'hoag',
 'apeshyt',
 'revisions',
 'champaign',
 'exploiting',
 'birman',
 'pelt',
 'primitives',
 'satya',
 'carnage',
 'uneasily',
 'advances',
 'baichwal',
 'malibu',
 'motivation',
 'lawyerly',
 'wali',
 'calmness',
 'somewhat',
 'moviemanmenzel',
 'buisnesswoman',
 'aircraft',
 'start',
 'divider',
 'microsoft',
 'lebrock',
 'rudimentary',
 'anu',
 'feign',
 'powell',
 'sweeter',
 'untypically',
 'together',
 'amble',
 'demin',
 'epitomised',
 'therefore',
 'tousle',
 'linden',
 'cassie',
 'ga',
 'dealing',
 'cultic',
 'dunning',
 'emblazoned',
 'ronin',
 'cheers',
 'snuggest',
 'illogically',
 'coincided',
 'walentin',
 'consumerist',
 'martyrdom',
 'misleadingly',
 'innovatory',
 'affirm',
 'anime',
 'doorknob',
 'resemblance',
 'gulshan',
 'ambling',
 'figueroa',
 'guccini',
 'detained',
 'controversy',
 'dessert',
 'stocky',
 'khakhi',
 'farman',
 'lindon',
 'postal',
 'misanthrope',
 'cran',
 'grisham',
 'brims',
 'magnet',
 'silencer',
 'eisenstein',
 'haev',
 'yanking',
 'remsen',
 'diverges',
 'exclusives',
 'genders',
 'sparce',
 'highwayman',
 'cattlemen',
 'quibbling',
 'woundings',
 'waxed',
 'brandauer',
 'jcpenney',
 'complained',
 'moo',
 'whys',
 'colorful',
 'humdrum',
 'masanori',
 'awareness',
 'parentally',
 'muerte',
 'outcry',
 'maru',
 'noshame',
 'keir',
 'conchatta',
 'tidying',
 'skirts',
 'effecting',
 'spang',
 'mulls',
 'closets',
 'doldrums',
 'precision',
 'coverings',
 'desposal',
 'chrismas',
 'serrador',
 'beguiles',
 'shortchanging',
 'reluctant',
 'multitude',
 'rehabilitated',
 'stiller',
 'conforming',
 'retellings',
 'performer',
 'chronicle',
 'languishing',
 'blowtorches',
 'protege',
 'kitt',
 'farrell',
 'obtained',
 'intermesh',
 'repay',
 'underwhelmed',
 'obtuseness',
 'moonshining',
 'shift',
 'hood',
 'juarassic',
 'imperceptibly',
 'borderline',
 'describes',
 'plausibly',
 'outted',
 'pursuant',
 'straining',
 'whow',
 'fettle',
 'gleib',
 'oja',
 'wades',
 'sandro',
 'stallone',
 'gojn',
 'yipe',
 'communicable',
 'kroual',
 'muzzle',
 'luminously',
 'tupac',
 'cardassian',
 'fleeting',
 'chethe',
 'younes',
 'raison',
 'choule',
 'nerve',
 'bountiful',
 'warners',
 'chandra',
 'rankles',
 'contra',
 'settles',
 'cleaning',
 'fethard',
 'incantation',
 'julianna',
 'killling',
 'hazmat',
 'eighty',
 'joyous',
 'klute',
 'ostracized',
 'ziman',
 'scaffolding',
 'excepted',
 'wake',
 'buffs',
 'patients',
 'viva',
 'rosco',
 'lavatory',
 'house',
 'keypunch',
 'seen',
 'demoralise',
 'messily',
 'impedimenta',
 'lazers',
 'fathered',
 'photography',
 'consents',
 'piere',
 'sinyard',
 'straightens',
 'orphaned',
 'hardened',
 'tudman',
 'codependent',
 'uni',
 'cinevista',
 'crteil',
 'establishment',
 'lick',
 'psychological',
 'tsuyako',
 'scarwid',
 'lourie',
 'constraints',
 'abromowitz',
 'hark',
 'coitus',
 'vogues',
 'catharine',
 'ghidrah',
 'earpeircing',
 'bwp',
 'visits',
 'eraser',
 'preconceived',
 'avaricious',
 'humored',
 'expresso',
 'chinoiserie',
 'impulsiveness',
 'bataille',
 'directors',
 'velociraptor',
 'posteriorly',
 'uruguay',
 'contaminated',
 'raphael',
 'papi',
 'someome',
 'sprouts',
 'bernarda',
 'porthos',
 'concurrently',
 'sensationalising',
 'definative',
 'inevitabally',
 'youngest',
 'ditzy',
 'cartwright',
 'eclipsed',
 'all',
 'marj',
 'panjab',
 'colada',
 'communicative',
 'wainrights',
 'dependent',
 'roarke',
 'sandoval',
 'underlined',
 'venantino',
 'miri',
 'dice',
 'newtypes',
 'insisted',
 'babbitt',
 'disgracing',
 'tyrant',
 'superstar',
 'mak',
 'streisandy',
 'joby',
 'condecension',
 'lewtons',
 'mohnish',
 'ravenously',
 'concede',
 'eppes',
 'gavriil',
 'burt',
 'paleontologist',
 'finds',
 'overrated',
 'kinjite',
 'dalmatians',
 'exiled',
 'doco',
 'quatre',
 'commanders',
 'trimmings',
 'janeane',
 'cake',
 'kfc',
 'pause',
 'holliday',
 'confections',
 'stv',
 'refuting',
 'ping',
 'cayetana',
 'haze',
 'carito',
 'johansson',
 'excluded',
 'backsliding',
 'lager',
 'psfrollo',
 ...]

In [17]:

    

import numpy as np

layer_0 = np.zeros((1,vocab_size))
layer_0


    

    
Out[17]:





array([[ 0.,  0.,  0., ...,  0.,  0.,  0.]])

In [18]:

    

from IPython.display import Image
Image(filename='sentiment_network.png')


    

    
Out[18]:

In [19]:

    

word2index = {}

for i,word in enumerate(vocab):
    word2index[word] = i
word2index


    

    
Out[19]:





{'': 0,
 'sarat': 1,
 'augers': 2,
 'automation': 3,
 'duller': 4,
 'hippo': 5,
 'addictive': 61771,
 'isolated': 6,
 'dramatisations': 12266,
 'vanquishing': 7,
 'lorraina': 8,
 'hat': 9,
 'merkel': 10,
 'papal': 11,
 'visionary': 12,
 'broadhurst': 13,
 'trespasses': 15,
 'ok': 16,
 'inventive': 17,
 'freire': 18,
 'bens': 19,
 'jimnez': 20,
 'dissapointment': 21,
 'hasidic': 22,
 'cussed': 24,
 'carstone': 25,
 'recast': 26,
 'sympathizing': 27,
 'clenteen': 28,
 'puzzlers': 30,
 'captives': 67295,
 'explosively': 31,
 'nutcases': 32,
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 'holliday': 987,
 'refuting': 990,
 'stadium': 72138,
 'charted': 37568,
 'cayetana': 992,
 'demonicus': 37218,
 'haze': 993,
 'carito': 994,
 'johansson': 995,
 'coachella': 71378,
 'excluded': 996,
 'backsliding': 997,
 'lager': 998,
 'psfrollo': 999,
 'myazaki': 1000,
 'susi': 37220,
 'adnausem': 24759,
 'marzia': 1001,
 'persevered': 1002,
 'nay': 1003,
 'bigtime': 12428,
 'puny': 178,
 'stillness': 12431,
 'kristan': 1005,
 'drat': 1008,
 'kodak': 1007,
 'emma': 1009,
 'mutated': 1010,
 'backett': 1011,
 'dateness': 24762,
 'liebe': 1012,
 'elm': 1013,
 'rotoscoping': 1014,
 'widely': 181,
 'vain': 28593,
 'lodz': 1015,
 'obsesses': 61930,
 'gatiss': 1018,
 'moviepass': 1017,
 'presson': 1019,
 'sheer': 61931,
 'messianistic': 47471,
 'huddling': 50803,
 'impetuous': 1020,
 'douses': 12437,
 'constrictions': 1022,
 'misanthropes': 1023,
 'horvitz': 61934,
 'kalamazoo': 1024,
 'impromptu': 44234,
 'eric': 1025,
 'barely': 1027,
 'jellyfish': 1028,
 'rimmed': 186,
 ...}

In [20]:

    

def update_input_layer(review):
    
    global layer_0
    
    # clear out previous state, reset the layer to be all 0s
    layer_0 *= 0
    for word in review.split(" "):
        layer_0[0][word2index[word]] += 1

update_input_layer(reviews[0])


    

In [21]:

    

layer_0


    

    
Out[21]:





array([[ 18.,   0.,   0., ...,   0.,   0.,   0.]])

In [22]:

    

def get_target_for_label(label):
    if(label == 'POSITIVE'):
        return 1
    else:
        return 0


    

In [23]:

    

labels[0]


    

    
Out[23]:





'POSITIVE'

In [24]:

    

get_target_for_label(labels[0])


    

    
Out[24]:





1

In [25]:

    

labels[1]


    

    
Out[25]:





'NEGATIVE'

In [26]:

    

get_target_for_label(labels[1])


    

    
Out[26]:





0

In [27]:

    

import time
import sys
import numpy as np

# Let's tweak our network from before to model these phenomena
class SentimentNetwork:
    def __init__(self, reviews,labels,hidden_nodes = 10, learning_rate = 0.1):
       
        # set our random number generator 
        np.random.seed(1)
    
        self.pre_process_data(reviews, labels)
        
        self.init_network(len(self.review_vocab),hidden_nodes, 1, learning_rate)
        
        
    def pre_process_data(self, reviews, labels):
        
        review_vocab = set()
        for review in reviews:
            for word in review.split(" "):
                review_vocab.add(word)
        self.review_vocab = list(review_vocab)
        
        label_vocab = set()
        for label in labels:
            label_vocab.add(label)
        
        self.label_vocab = list(label_vocab)
        
        self.review_vocab_size = len(self.review_vocab)
        self.label_vocab_size = len(self.label_vocab)
        
        self.word2index = {}
        for i, word in enumerate(self.review_vocab):
            self.word2index[word] = i
        
        self.label2index = {}
        for i, label in enumerate(self.label_vocab):
            self.label2index[label] = i
         
        
    def init_network(self, input_nodes, hidden_nodes, output_nodes, learning_rate):
        # Set number of nodes in input, hidden and output layers.
        self.input_nodes = input_nodes
        self.hidden_nodes = hidden_nodes
        self.output_nodes = output_nodes

        # Initialize weights
        self.weights_0_1 = np.zeros((self.input_nodes,self.hidden_nodes))
    
        self.weights_1_2 = np.random.normal(0.0, self.output_nodes**-0.5, 
                                                (self.hidden_nodes, self.output_nodes))
        
        self.learning_rate = learning_rate
        
        self.layer_0 = np.zeros((1,input_nodes))
    
        
    def update_input_layer(self,review):

        # clear out previous state, reset the layer to be all 0s
        self.layer_0 *= 0
        for word in review.split(" "):
            if(word in self.word2index.keys()):
                
                # Equal to 1 to balance data
                self.layer_0[0][self.word2index[word]] = 1
                
    def get_target_for_label(self,label):
        if(label == 'POSITIVE'):
            return 1
        else:
            return 0
        
    def sigmoid(self,x):
        return 1 / (1 + np.exp(-x))
    
    
    def sigmoid_output_2_derivative(self,output):
        return output * (1 - output)
    
    def train(self, training_reviews, training_labels):
        
        assert(len(training_reviews) == len(training_labels))
        
        correct_so_far = 0
        
        start = time.time()
        
        for i in range(len(training_reviews)):
            
            review = training_reviews[i]
            label = training_labels[i]
            
            #### Implement the forward pass here ####
            ### Forward pass ###

            # Input Layer
            self.update_input_layer(review)

            # Hidden layer
            layer_1 = self.layer_0.dot(self.weights_0_1)

            # Output layer
            layer_2 = self.sigmoid(layer_1.dot(self.weights_1_2))

            #### Implement the backward pass here ####
            ### Backward pass ###

            # TODO: Output error
            layer_2_error = layer_2 - self.get_target_for_label(label) # Output layer error is the difference between desired target and actual output.
            layer_2_delta = layer_2_error * self.sigmoid_output_2_derivative(layer_2)

            # TODO: Backpropagated error
            layer_1_error = layer_2_delta.dot(self.weights_1_2.T) # errors propagated to the hidden layer
            layer_1_delta = layer_1_error # hidden layer gradients - no nonlinearity so it's the same as the error

            # TODO: Update the weights
            self.weights_1_2 -= layer_1.T.dot(layer_2_delta) * self.learning_rate # update hidden-to-output weights with gradient descent step
            self.weights_0_1 -= self.layer_0.T.dot(layer_1_delta) * self.learning_rate # update input-to-hidden weights with gradient descent step

            if(np.abs(layer_2_error) < 0.5):
                correct_so_far += 1
            
            reviews_per_second = i / float(time.time() - start)
            
            sys.stdout.write("\rProgress:" + str(100 * i/float(len(training_reviews)))[:4] + "% Speed(reviews/sec):" + str(reviews_per_second)[0:5] + " #Correct:" + str(correct_so_far) + " #Trained:" + str(i+1) + " Training Accuracy:" + str(correct_so_far * 100 / float(i+1))[:4] + "%")
            if(i % 2500 == 0):
                print("")
    
    def test(self, testing_reviews, testing_labels):
        
        correct = 0
        
        start = time.time()
        
        for i in range(len(testing_reviews)):
            pred = self.run(testing_reviews[i])
            if(pred == testing_labels[i]):
                correct += 1
            
            reviews_per_second = i / float(time.time() - start)
            
            sys.stdout.write("\rProgress:" + str(100 * i/float(len(testing_reviews)))[:4] \
                             + "% Speed(reviews/sec):" + str(reviews_per_second)[0:5] \
                            + "% #Correct:" + str(correct) + " #Tested:" + str(i+1) + " Testing Accuracy:" + str(correct * 100 / float(i+1))[:4] + "%")
    
    def run(self, review):
        
        # Input Layer
        self.update_input_layer(review.lower())

        # Hidden layer
        layer_1 = self.layer_0.dot(self.weights_0_1)

        # Output layer
        layer_2 = self.sigmoid(layer_1.dot(self.weights_1_2))
        
        if(layer_2[0] > 0.5):
            return "POSITIVE"
        else:
            return "NEGATIVE"


    

In [28]:

    

mlp = SentimentNetwork(reviews[:-1000],labels[:-1000], learning_rate=0.1)


    

In [29]:

    

# evaluate our model before training (just to show how horrible it is)
mlp.test(reviews[-1000:],labels[-1000:])


    

    




Progress:99.9% Speed(reviews/sec):1090.% #Correct:500 #Tested:1000 Testing Accuracy:50.0%

In [30]:

    

# train the network
mlp.train(reviews[:-1000],labels[:-1000])


    

    




Progress:0.0% Speed(reviews/sec):0.0 #Correct:0 #Trained:1 Training Accuracy:0.0%
Progress:10.4% Speed(reviews/sec):118.7 #Correct:1814 #Trained:2501 Training Accuracy:72.5%
Progress:20.8% Speed(reviews/sec):119.8 #Correct:3793 #Trained:5001 Training Accuracy:75.8%
Progress:31.2% Speed(reviews/sec):120.5 #Correct:5865 #Trained:7501 Training Accuracy:78.1%
Progress:41.6% Speed(reviews/sec):119.3 #Correct:8012 #Trained:10001 Training Accuracy:80.1%
Progress:44.4% Speed(reviews/sec):118.7 #Correct:8580 #Trained:10679 Training Accuracy:80.3%





    




---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
<ipython-input-30-d0f5d85ad402> in <module>()
      1 # train the network
----> 2 mlp.train(reviews[:-1000],labels[:-1000])

<ipython-input-27-88b4a23b94fd> in train(self, training_reviews, training_labels)
     98 
     99             # Input Layer
--> 100             self.update_input_layer(review)
    101 
    102             # Hidden layer

<ipython-input-27-88b4a23b94fd> in update_input_layer(self, review)
     61 
     62         # clear out previous state, reset the layer to be all 0s
---> 63         self.layer_0 *= 0
     64         for word in review.split(" "):
     65             if(word in self.word2index.keys()):

KeyboardInterrupt: 

In [63]:

    

mlp = SentimentNetwork(reviews[:-1000],labels[:-1000], learning_rate=0.01)


    

In [64]:

    

# train the network
mlp.train(reviews[:-1000],labels[:-1000])


    

    




Progress:0.0% Speed(reviews/sec):0.0 #Correct:0 #Trained:1 Training Accuracy:0.0%
Progress:10.4% Speed(reviews/sec):96.39 #Correct:1247 #Trained:2501 Training Accuracy:49.8%
Progress:20.8% Speed(reviews/sec):99.31 #Correct:2497 #Trained:5001 Training Accuracy:49.9%
Progress:22.8% Speed(reviews/sec):99.02 #Correct:2735 #Trained:5476 Training Accuracy:49.9%





    




---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
<ipython-input-64-d0f5d85ad402> in <module>()
      1 # train the network
----> 2 mlp.train(reviews[:-1000],labels[:-1000])

<ipython-input-59-6334c4ec4642> in train(self, training_reviews, training_labels)
    117             # TODO: Update the weights
    118             self.weights_1_2 -= layer_1.T.dot(layer_2_delta) * self.learning_rate # update hidden-to-output weights with gradient descent step
--> 119             self.weights_0_1 -= self.layer_0.T.dot(layer_1_delta) * self.learning_rate # update input-to-hidden weights with gradient descent step
    120 
    121             if(np.abs(layer_2_error) < 0.5):

KeyboardInterrupt: 

In [65]:

    

mlp = SentimentNetwork(reviews[:-1000],labels[:-1000], learning_rate=0.001)


    

In [66]:

    

# train the network
mlp.train(reviews[:-1000],labels[:-1000])


    

    




Progress:0.0% Speed(reviews/sec):0.0 #Correct:0 #Trained:1 Training Accuracy:0.0%
Progress:10.4% Speed(reviews/sec):98.77 #Correct:1267 #Trained:2501 Training Accuracy:50.6%
Progress:20.8% Speed(reviews/sec):98.79 #Correct:2640 #Trained:5001 Training Accuracy:52.7%
Progress:31.2% Speed(reviews/sec):98.58 #Correct:4109 #Trained:7501 Training Accuracy:54.7%
Progress:41.6% Speed(reviews/sec):93.78 #Correct:5638 #Trained:10001 Training Accuracy:56.3%
Progress:52.0% Speed(reviews/sec):91.76 #Correct:7246 #Trained:12501 Training Accuracy:57.9%
Progress:62.5% Speed(reviews/sec):92.42 #Correct:8841 #Trained:15001 Training Accuracy:58.9%
Progress:69.4% Speed(reviews/sec):92.58 #Correct:9934 #Trained:16668 Training Accuracy:59.5%





    




---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
<ipython-input-66-d0f5d85ad402> in <module>()
      1 # train the network
----> 2 mlp.train(reviews[:-1000],labels[:-1000])

<ipython-input-59-6334c4ec4642> in train(self, training_reviews, training_labels)
    117             # TODO: Update the weights
    118             self.weights_1_2 -= layer_1.T.dot(layer_2_delta) * self.learning_rate # update hidden-to-output weights with gradient descent step
--> 119             self.weights_0_1 -= self.layer_0.T.dot(layer_1_delta) * self.learning_rate # update input-to-hidden weights with gradient descent step
    120 
    121             if(np.abs(layer_2_error) < 0.5):

KeyboardInterrupt: 

In [67]:

    

from IPython.display import Image
Image(filename='sentiment_network.png')


    

    
Out[67]:

In [70]:

    

def update_input_layer(review):
    
    global layer_0
    
    # clear out previous state, reset the layer to be all 0s
    layer_0 *= 0
    for word in review.split(" "):
        layer_0[0][word2index[word]] += 1

update_input_layer(reviews[0])


    

In [71]:

    

layer_0


    

    
Out[71]:





array([[ 18.,   0.,   0., ...,   0.,   0.,   0.]])

In [79]:

    

review_counter = Counter()


    

In [80]:

    

for word in reviews[0].split(" "):
    review_counter[word] += 1


    

In [81]:

    

review_counter.most_common()


    

    
Out[81]:





[('.', 27),
 ('', 18),
 ('the', 9),
 ('to', 6),
 ('i', 5),
 ('high', 5),
 ('is', 4),
 ('of', 4),
 ('a', 4),
 ('bromwell', 4),
 ('teachers', 4),
 ('that', 4),
 ('their', 2),
 ('my', 2),
 ('at', 2),
 ('as', 2),
 ('me', 2),
 ('in', 2),
 ('students', 2),
 ('it', 2),
 ('student', 2),
 ('school', 2),
 ('through', 1),
 ('insightful', 1),
 ('ran', 1),
 ('years', 1),
 ('here', 1),
 ('episode', 1),
 ('reality', 1),
 ('what', 1),
 ('far', 1),
 ('t', 1),
 ('saw', 1),
 ('s', 1),
 ('repeatedly', 1),
 ('isn', 1),
 ('closer', 1),
 ('and', 1),
 ('fetched', 1),
 ('remind', 1),
 ('can', 1),
 ('welcome', 1),
 ('line', 1),
 ('your', 1),
 ('survive', 1),
 ('teaching', 1),
 ('satire', 1),
 ('classic', 1),
 ('who', 1),
 ('age', 1),
 ('knew', 1),
 ('schools', 1),
 ('inspector', 1),
 ('comedy', 1),
 ('down', 1),
 ('about', 1),
 ('pity', 1),
 ('m', 1),
 ('all', 1),
 ('adults', 1),
 ('see', 1),
 ('think', 1),
 ('situation', 1),
 ('time', 1),
 ('pomp', 1),
 ('lead', 1),
 ('other', 1),
 ('much', 1),
 ('many', 1),
 ('which', 1),
 ('one', 1),
 ('profession', 1),
 ('programs', 1),
 ('same', 1),
 ('some', 1),
 ('such', 1),
 ('pettiness', 1),
 ('immediately', 1),
 ('expect', 1),
 ('financially', 1),
 ('recalled', 1),
 ('tried', 1),
 ('whole', 1),
 ('right', 1),
 ('life', 1),
 ('cartoon', 1),
 ('scramble', 1),
 ('sack', 1),
 ('believe', 1),
 ('when', 1),
 ('than', 1),
 ('burn', 1),
 ('pathetic', 1)]