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),
 ('far', 1358),
 ('course', 1358),
 ('john', 1350),
 ('rather', 1340),
 ('isn', 1328),
 ('ll', 1326),
 ('later', 1324),
 ('dvd', 1324),
 ('whole', 1310),
 ('war', 1310),
 ('d', 1307),
 ('found', 1306),
 ('away', 1306),
 ('screen', 1305),
 ('nothing', 1300),
 ('year', 1297),
 ('once', 1296),
 ('hard', 1294),
 ('together', 1280),
 ('set', 1277),
 ('am', 1277),
 ('having', 1266),
 ('making', 1265),
 ('place', 1263),
 ('might', 1260),
 ('comes', 1260),
 ('sure', 1253),
 ('american', 1248),
 ('play', 1245),
 ('kind', 1244),
 ('perfect', 1242),
 ('takes', 1242),
 ('performances', 1237),
 ('himself', 1230),
 ('worth', 1221),
 ('everyone', 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),
 ('believe', 1071),
 ('guy', 1071),
 ('top', 1063),
 ('amazing', 1058),
 ('hollywood', 1056),
 ('looking', 1053),
 ('main', 1044),
 ('definitely', 1043),
 ('gives', 1031),
 ('home', 1029),
 ('seem', 1028),
 ('episode', 1023),
 ('audience', 1020),
 ('sense', 1020),
 ('truly', 1017),
 ('special', 1011),
 ('second', 1009),
 ('short', 1009),
 ('fan', 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),
 ('live', 934),
 ('less', 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),
 ('picture', 833),
 ('children', 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),
 ('based', 781),
 ('rest', 781),
 ('try', 780),
 ('dead', 776),
 ('hope', 775),
 ('strong', 768),
 ('white', 765),
 ('tell', 759),
 ('itself', 758),
 ('half', 753),
 ('person', 749),
 ('sometimes', 746),
 ('past', 744),
 ('start', 744),
 ('genre', 743),
 ('beginning', 739),
 ('final', 739),
 ('town', 738),
 ('art', 734),
 ('humor', 732),
 ('game', 732),
 ('yes', 731),
 ('idea', 731),
 ('late', 730),
 ('becomes', 729),
 ('despite', 729),
 ('able', 726),
 ('case', 726),
 ('money', 723),
 ('child', 721),
 ('completely', 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),
 ('name', 683),
 ('either', 683),
 ('doing', 677),
 ('turns', 674),
 ('wants', 671),
 ('close', 671),
 ('title', 669),
 ('wrong', 668),
 ('went', 666),
 ('james', 665),
 ('evil', 659),
 ('budget', 657),
 ('episodes', 657),
 ('relationship', 655),
 ('fantastic', 653),
 ('piece', 653),
 ('david', 651),
 ('turn', 648),
 ('murder', 646),
 ('parts', 645),
 ('brother', 644),
 ('absolutely', 643),
 ('head', 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),
 ('including', 627),
 ('example', 627),
 ('known', 625),
 ('musical', 625),
 ('chance', 621),
 ('score', 620),
 ('already', 619),
 ('feeling', 619),
 ('hit', 619),
 ('voice', 615),
 ('moment', 612),
 ('living', 612),
 ('low', 610),
 ('supporting', 610),
 ('ago', 609),
 ('themselves', 608),
 ('reality', 605),
 ('hilarious', 605),
 ('jack', 604),
 ('told', 603),
 ('hand', 601),
 ('quality', 600),
 ('moving', 600),
 ('dialogue', 600),
 ('song', 599),
 ('happy', 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),
 ('whose', 578),
 ('type', 578),
 ('sound', 578),
 ('view', 573),
 ('enjoyable', 573),
 ('number', 572),
 ('romantic', 572),
 ('husband', 572),
 ('daughter', 572),
 ('documentary', 571),
 ('self', 570),
 ('superb', 569),
 ('modern', 569),
 ('took', 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),
 ('run', 552),
 ('career', 552),
 ('country', 552),
 ('heard', 550),
 ('season', 550),
 ('greatest', 549),
 ('girls', 549),
 ('etc', 547),
 ('care', 546),
 ('starts', 545),
 ('english', 542),
 ('killer', 541),
 ('tale', 540),
 ('guys', 540),
 ('totally', 540),
 ('animation', 540),
 ('usual', 539),
 ('miss', 535),
 ('opinion', 535),
 ('easy', 531),
 ('violence', 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),
 ('york', 513),
 ('novel', 513),
 ('alone', 512),
 ('problem', 512),
 ('attention', 509),
 ('involved', 508),
 ('kill', 507),
 ('extremely', 507),
 ('seemed', 506),
 ('hero', 505),
 ('french', 505),
 ('rock', 504),
 ('stuff', 501),
 ('wish', 499),
 ('begins', 498),
 ('taken', 497),
 ('sad', 497),
 ('ways', 496),
 ('richard', 495),
 ('knows', 494),
 ('atmosphere', 493),
 ('similar', 491),
 ('surprised', 491),
 ('taking', 491),
 ('car', 491),
 ('george', 490),
 ('perfectly', 490),
 ('across', 489),
 ('team', 489),
 ('eye', 489),
 ('sequence', 489),
 ('room', 488),
 ('due', 488),
 ('among', 488),
 ('serious', 488),
 ('powerful', 488),
 ('strange', 487),
 ('order', 487),
 ('cannot', 487),
 ('b', 487),
 ('beauty', 486),
 ('famous', 485),
 ('happened', 484),
 ('tries', 484),
 ('herself', 484),
 ('myself', 484),
 ('class', 483),
 ('four', 482),
 ('cool', 481),
 ('release', 479),
 ('anyway', 479),
 ('theme', 479),
 ('opening', 478),
 ('entertainment', 477),
 ('slow', 475),
 ('ends', 475),
 ('unique', 475),
 ('exactly', 475),
 ('easily', 474),
 ('level', 474),
 ('o', 474),
 ('red', 474),
 ('interest', 472),
 ('happen', 471),
 ('crime', 470),
 ('viewing', 468),
 ('sets', 467),
 ('memorable', 467),
 ('stop', 466),
 ('group', 466),
 ('problems', 463),
 ('dance', 463),
 ('working', 463),
 ('sister', 463),
 ('message', 463),
 ('knew', 462),
 ('mystery', 461),
 ('nature', 461),
 ('bring', 460),
 ('believable', 459),
 ('thinking', 459),
 ('brought', 459),
 ('mostly', 458),
 ('disney', 457),
 ('couldn', 457),
 ('society', 456),
 ('lady', 455),
 ('within', 455),
 ('blood', 454),
 ('parents', 453),
 ('upon', 453),
 ('viewers', 453),
 ('meets', 452),
 ('form', 452),
 ('peter', 452),
 ('tom', 452),
 ('usually', 452),
 ('soundtrack', 452),
 ('local', 450),
 ('certain', 448),
 ('follow', 448),
 ('whether', 447),
 ('possible', 446),
 ('emotional', 445),
 ('killed', 444),
 ('above', 444),
 ('de', 444),
 ('god', 443),
 ('middle', 443),
 ('needs', 442),
 ('happens', 442),
 ('flick', 442),
 ('masterpiece', 441),
 ('period', 440),
 ('major', 440),
 ('named', 439),
 ('haven', 439),
 ('particular', 438),
 ('th', 438),
 ('earth', 437),
 ('feature', 437),
 ('stand', 436),
 ('words', 435),
 ('typical', 435),
 ('elements', 433),
 ('obviously', 433),
 ('romance', 431),
 ('jane', 430),
 ('yourself', 427),
 ('showing', 427),
 ('brings', 426),
 ('fantasy', 426),
 ('guess', 423),
 ('america', 423),
 ('unfortunately', 422),
 ('huge', 422),
 ('indeed', 421),
 ('running', 421),
 ('talent', 420),
 ('stage', 419),
 ('started', 418),
 ('leads', 417),
 ('sweet', 417),
 ('japanese', 417),
 ('poor', 416),
 ('deal', 416),
 ('incredible', 413),
 ('personal', 413),
 ('fast', 412),
 ('became', 410),
 ('deep', 410),
 ('hours', 409),
 ('giving', 408),
 ('nearly', 408),
 ('dream', 408),
 ('clearly', 407),
 ('turned', 407),
 ('obvious', 406),
 ('near', 406),
 ('cut', 405),
 ('surprise', 405),
 ('era', 404),
 ('body', 404),
 ('hour', 403),
 ('female', 403),
 ('five', 403),
 ('note', 399),
 ('learn', 398),
 ('truth', 398),
 ('except', 397),
 ('feels', 397),
 ('match', 397),
 ('tony', 397),
 ('filmed', 394),
 ('clear', 394),
 ('complete', 394),
 ('street', 393),
 ('eventually', 393),
 ('keeps', 393),
 ('older', 393),
 ('lots', 393),
 ('buy', 392),
 ('william', 391),
 ('stewart', 391),
 ('fall', 390),
 ('joe', 390),
 ('meet', 390),
 ('unlike', 389),
 ('talking', 389),
 ('shots', 389),
 ('rating', 389),
 ('difficult', 389),
 ('dramatic', 388),
 ('means', 388),
 ('situation', 386),
 ('wonder', 386),
 ('present', 386),
 ('appears', 386),
 ('subject', 386),
 ('comments', 385),
 ('general', 383),
 ('sequences', 383),
 ('lee', 383),
 ('points', 382),
 ('earlier', 382),
 ('gone', 379),
 ('check', 379),
 ('suspense', 378),
 ('recommended', 378),
 ('ten', 378),
 ('third', 377),
 ('business', 377),
 ('talk', 375),
 ('leaves', 375),
 ('beyond', 375),
 ('portrayal', 374),
 ('beautifully', 373),
 ('single', 372),
 ('bill', 372),
 ('plenty', 371),
 ('word', 371),
 ('whom', 370),
 ('falls', 370),
 ('scary', 369),
 ('non', 369),
 ('figure', 369),
 ('battle', 369),
 ('using', 368),
 ('return', 368),
 ('doubt', 367),
 ('add', 367),
 ('hear', 366),
 ('solid', 366),
 ('success', 366),
 ('jokes', 365),
 ('oh', 365),
 ('touching', 365),
 ('political', 365),
 ('hell', 364),
 ('awesome', 364),
 ('boys', 364),
 ('sexual', 362),
 ('recently', 362),
 ('dog', 362),
 ('please', 361),
 ('wouldn', 361),
 ('straight', 361),
 ('features', 361),
 ('forget', 360),
 ('setting', 360),
 ('lack', 360),
 ('married', 359),
 ('mark', 359),
 ('social', 357),
 ('interested', 356),
 ('adventure', 356),
 ('actual', 355),
 ('terrific', 355),
 ('sees', 355),
 ('brothers', 355),
 ('move', 354),
 ('call', 354),
 ('various', 353),
 ('theater', 353),
 ('dr', 353),
 ('animated', 352),
 ('western', 351),
 ('baby', 350),
 ('space', 350),
 ('leading', 348),
 ('disappointed', 348),
 ('portrayed', 346),
 ('aren', 346),
 ('screenplay', 345),
 ('smith', 345),
 ('towards', 344),
 ('hate', 344),
 ('noir', 343),
 ('outstanding', 342),
 ('decent', 342),
 ('kelly', 342),
 ('directors', 341),
 ('journey', 341),
 ('none', 340),
 ('looked', 340),
 ('effective', 340),
 ('storyline', 339),
 ('caught', 339),
 ('sci', 339),
 ('fi', 339),
 ('cold', 339),
 ('mary', 339),
 ('rich', 338),
 ('charming', 338),
 ('popular', 337),
 ('rare', 337),
 ('manages', 337),
 ('harry', 337),
 ('spirit', 336),
 ('appreciate', 335),
 ('open', 335),
 ('moves', 334),
 ('basically', 334),
 ('acted', 334),
 ('inside', 333),
 ('boring', 333),
 ('century', 333),
 ('mention', 333),
 ('deserves', 333),
 ('subtle', 333),
 ('pace', 333),
 ('familiar', 332),
 ('background', 332),
 ('ben', 331),
 ('creepy', 330),
 ('supposed', 330),
 ('secret', 329),
 ('die', 328),
 ('jim', 328),
 ('question', 327),
 ('effect', 327),
 ('natural', 327),
 ('impressive', 326),
 ('rate', 326),
 ('language', 326),
 ('saying', 325),
 ('intelligent', 325),
 ('telling', 324),
 ('realize', 324),
 ('material', 324),
 ('scott', 324),
 ('singing', 323),
 ('dancing', 322),
 ('visual', 321),
 ('adult', 321),
 ('imagine', 321),
 ('kept', 320),
 ('office', 320),
 ('uses', 319),
 ('pure', 318),
 ('wait', 318),
 ('stunning', 318),
 ('review', 317),
 ('previous', 317),
 ('copy', 317),
 ('seriously', 317),
 ('reading', 316),
 ('create', 316),
 ('hot', 316),
 ('created', 316),
 ('magic', 316),
 ('somehow', 316),
 ('stay', 315),
 ('attempt', 315),
 ('escape', 315),
 ('crazy', 315),
 ('air', 315),
 ('frank', 315),
 ('hands', 314),
 ('filled', 313),
 ('expected', 312),
 ('average', 312),
 ('surprisingly', 312),
 ('complex', 311),
 ('quickly', 310),
 ('successful', 310),
 ('studio', 310),
 ('plus', 309),
 ('male', 309),
 ('co', 307),
 ('images', 306),
 ('casting', 306),
 ('following', 306),
 ('minute', 306),
 ('exciting', 306),
 ('members', 305),
 ('follows', 305),
 ('themes', 305),
 ('german', 305),
 ('reasons', 305),
 ('e', 305),
 ('touch', 304),
 ('edge', 304),
 ('free', 304),
 ('cute', 304),
 ('genius', 304),
 ('outside', 303),
 ('reviews', 302),
 ('admit', 302),
 ('ok', 302),
 ('younger', 302),
 ('fighting', 301),
 ('odd', 301),
 ('master', 301),
 ('recent', 300),
 ('thanks', 300),
 ('break', 300),
 ('comment', 300),
 ('apart', 299),
 ('emotions', 298),
 ('lovely', 298),
 ('begin', 298),
 ('doctor', 297),
 ('party', 297),
 ('italian', 297),
 ('la', 296),
 ('missed', 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),
 ('sullivan', 1.6094379124341003),
 ('extraordinary', 1.6094379124341003),
 ('hartley', 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),
 ('noir', 1.493925025312256),
 ('sidney', 1.493925025312256),
 ('outstanding', 1.4910053152089213),
 ('pleasantly', 1.4894785973551214),
 ('mann', 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),
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 ('gem', 1.3932148039644643),
 ('amazing', 1.3919815802404802),
 ('chilling', 1.3862943611198906),
 ('fisher', 1.3862943611198906),
 ('davies', 1.3862943611198906),
 ('captivating', 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),
 ('ned', 1.120591195386885),
 ('fay', 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),
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 ('walter', 1.022328983918479),
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 ('portrayal', 1.0189810189761024),
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 ('vhs', 0.97116734209998934),
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 ('lonely', 0.96873720724669754),
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 ('hal', 0.92953595862417571),
 ('louis', 0.92871325187271225),
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 ('allows', 0.92183091224977043),
 ('impact', 0.91815814604895041),
 ('italy', 0.91629073187415511),
 ('gradually', 0.91629073187415511),
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 ('spy', 0.91289514287301687),
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 ('funniest', 0.90078654533818991),
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 ('searching', 0.4393666597838457),
 ('rap', 0.43891304217570443),
 ('light', 0.43884433018199892),
 ('elizabeth', 0.43872232986464677),
 ('marry', 0.43861731542506488),
 ('learned', 0.43825493093115531),
 ('controversial', 0.43825493093115531),
 ('oz', 0.43825493093115531),
 ('slowly', 0.43785660389939979),
 ('comedic', 0.43721380642274466),
 ('wayne', 0.43721380642274466),
 ('thrilling', 0.43721380642274466),
 ('bridge', 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),
 ('movement', 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]:





['',
 'rohauer',
 'conclusive',
 'linnea',
 'stalked',
 'ramya',
 'twi',
 'clche',
 'darwininan',
 'seigner',
 'nrw',
 'henrietta',
 'roehler',
 'impossibly',
 'vhs',
 'vets',
 'jobyna',
 'riead',
 'disenchanted',
 'unearthly',
 'carrol',
 'evy',
 'livestock',
 'misery',
 'immigrating',
 'talia',
 'angeli',
 'subor',
 'piana',
 'lidl',
 'saul',
 'karrer',
 'grill',
 'gao',
 'phds',
 'kerchief',
 'spook',
 'ticks',
 'caiano',
 'beaker',
 'bighouse',
 'analogous',
 'vandamme',
 'fantastical',
 'garnier',
 'sabella',
 'fargas',
 'awaiting',
 'wildcat',
 'uninviting',
 'enveloping',
 'slips',
 'flightiness',
 'orville',
 'dillute',
 'overpopulated',
 'streed',
 'humperdink',
 'quipped',
 'wing',
 'suffering',
 'telegraphing',
 'roles',
 'meu',
 'marijauna',
 'runner',
 'hyman',
 'waltons',
 'goddamit',
 'avventura',
 'vassilis',
 'royal',
 'trejo',
 'sinking',
 'diahann',
 'farell',
 'raucous',
 'friedo',
 'travelers',
 'sadoul',
 'imperfect',
 'rashid',
 'ruining',
 'fatality',
 'delegate',
 'horsing',
 'pauley',
 'concludes',
 'conservatism',
 'celebrated',
 'apke',
 'basil',
 'fanfavorite',
 'unprofessionals',
 'wheaton',
 'formulates',
 'uuuuuugggghhhhh',
 'suzuki',
 'mouton',
 'drinking',
 'stribor',
 'lice',
 'gardiner',
 'speedy',
 'shriver',
 'joystick',
 'caprio',
 'fromage',
 'dv',
 'musee',
 'subsidies',
 'spewed',
 'crusade',
 'diabolique',
 'underwent',
 'joyrides',
 'belmont',
 'sidmarty',
 'hire',
 'mendolita',
 'legalize',
 'density',
 'ultramagnetic',
 'sisterly',
 'furs',
 'libertine',
 'chiseled',
 'starkest',
 'djakarta',
 'grammatically',
 'eurail',
 'stumble',
 'verducci',
 'flounder',
 'remarque',
 'reproduces',
 'leetle',
 'hassled',
 'entailing',
 'jogger',
 'xlr',
 'updating',
 'floor',
 'sink',
 'lifeguard',
 'geometrical',
 'brigand',
 'stretcher',
 'merge',
 'jhene',
 'katelin',
 'bananaman',
 'mouskouri',
 'waterway',
 'demonstrators',
 'richart',
 'mung',
 'dualities',
 'ucla',
 'counting',
 'phh',
 'cinemaniaks',
 'expurgated',
 'wave',
 'filmswere',
 'lokis',
 'kremhild',
 'likeliness',
 'trees',
 'eriksson',
 'guilgud',
 'pepa',
 'airliners',
 'alejandra',
 'ripa',
 'pitts',
 'waldo',
 'silsby',
 'mbbs',
 'jenney',
 'greenfield',
 'bethsheba',
 'vinessa',
 'mckoy',
 'rom',
 'misfires',
 'differentiating',
 'disordered',
 'supplant',
 'anh',
 'redlitch',
 'withdrawing',
 'painstakingly',
 'logical',
 'blonder',
 'fruity',
 'entities',
 'informing',
 'procedure',
 'irredeemably',
 'castcrew',
 'pedaling',
 'moloney',
 'baer',
 'unsteerable',
 'clogged',
 'shit',
 'removing',
 'creasy',
 'fanatical',
 'cinematically',
 'grunwald',
 'opportunism',
 'marnack',
 'bleedmedry',
 'aproned',
 'kiosk',
 'wasabi',
 'splendour',
 'disagree',
 'digestive',
 'stinky',
 'critiquing',
 'marshy',
 'intertwine',
 'satchwell',
 'capitalised',
 'moonshiners',
 'fangorn',
 'perceval',
 'generating',
 'hayseed',
 'dates',
 'erian',
 'peerce',
 'smolders',
 'confidence',
 'burlesques',
 'ventilating',
 'narcolepsy',
 'hing',
 'thief',
 'argo',
 'montazuma',
 'fatter',
 'fye',
 'savage',
 'hemorrhage',
 'katanas',
 'moviejust',
 'dislocated',
 'soooooooo',
 'waylan',
 'dubois',
 'grunting',
 'tallin',
 'marauding',
 'homily',
 'stub',
 'donati',
 'twos',
 'bloodiness',
 'hackenstein',
 'attired',
 'rightous',
 'homework',
 'myddleton',
 'namby',
 'nanobots',
 'rationalized',
 'pique',
 'mwuhahahaa',
 'standardsmoderately',
 'econovan',
 'astros',
 'tlb',
 'mastantonio',
 'polarisdib',
 'scarecreow',
 'seafood',
 'enlivens',
 'inconsiderate',
 'rwandese',
 'fallafel',
 'ragona',
 'leonowens',
 'focussing',
 'eberhard',
 'pricelessly',
 'torenstra',
 'phonies',
 'showsit',
 'qaulen',
 'elegantly',
 'organiser',
 'seized',
 'tanna',
 'jilted',
 'grabs',
 'goya',
 'rotted',
 'dots',
 'travelled',
 'surnamed',
 'fumbled',
 'angered',
 'overacting',
 'impending',
 'hare',
 'railly',
 'fopington',
 'macist',
 'thuy',
 'gorehound',
 'contributors',
 'impartially',
 'streams',
 'survivor',
 'cotta',
 'cutely',
 'aauugghh',
 'lobotomy',
 'woodworm',
 'utlimately',
 'scheme',
 'aryana',
 'texas',
 'artemesia',
 'unrecognized',
 'reared',
 'castrati',
 'diplomacy',
 'countdowns',
 'rogge',
 'niggers',
 'disjointing',
 'closed',
 'sheets',
 'profiled',
 'federally',
 'lasseter',
 'often',
 'summit',
 'teffe',
 'establishing',
 'occassionaly',
 'meditating',
 'federico',
 'chor',
 'lambert',
 'dissuaded',
 'fuhgeddaboudit',
 'draco',
 'cowgirl',
 'beethtoven',
 'willona',
 'ireland',
 'insulated',
 'clatch',
 'liquor',
 'yds',
 'pts',
 'plumping',
 'sue',
 'baltimore',
 'notting',
 'ditsy',
 'evoking',
 'kibbutz',
 'ravensback',
 'promising',
 'michael',
 'vapor',
 'studding',
 'jermaine',
 'windbag',
 'cannibalized',
 'kindness',
 'dictate',
 'natale',
 'whizbang',
 'checking',
 'trout',
 'crowds',
 'bounder',
 'lina',
 'carrere',
 'divinity',
 'colossal',
 'performing',
 'yukking',
 'pragmatist',
 'shurka',
 'limb',
 'psa',
 'nicholette',
 'douglas',
 'lagos',
 'chronologically',
 'indirect',
 'purbs',
 'smothers',
 'mallrats',
 'paltrow',
 'hazed',
 'runmanian',
 'intelligensia',
 'sailors',
 'fathoming',
 'holo',
 'redeem',
 'doiiing',
 'nay',
 'illnesses',
 'pardon',
 'microfiche',
 'fetuccini',
 'aragorns',
 'chief',
 'befriended',
 'uncompromised',
 'faceless',
 'wiseguys',
 'alliances',
 'evoked',
 'conversion',
 'cutters',
 'salient',
 'responsiveness',
 'sticky',
 'wcw',
 'ans',
 'bassey',
 'bathrooms',
 'expense',
 'investigates',
 'moeurs',
 'smita',
 'waist',
 'sushmita',
 'developmental',
 'anonimul',
 'eyes',
 'secert',
 'stanza',
 'enoch',
 'pizazz',
 'watterman',
 'mahnaz',
 'sexploits',
 'disloyalty',
 'glom',
 'preformed',
 'groundbraking',
 'apparel',
 'telly',
 'mankin',
 'sensationally',
 'hiking',
 'wayand',
 'mikail',
 'bloom',
 'dominators',
 'ralph',
 'radicals',
 'achieve',
 'infidelity',
 'talosian',
 'endorsed',
 'froid',
 'atilla',
 'kentucky',
 'radtha',
 'cynical',
 'referenced',
 'dowling',
 'juno',
 'taxidermy',
 'belong',
 'recognizing',
 'omirus',
 'verged',
 'sidekick',
 'provensa',
 'pide',
 'dirt',
 'zoo',
 'innovating',
 'chic',
 'lassie',
 'devourer',
 'tibetian',
 'overdirection',
 'ontop',
 'helps',
 'jregrd',
 'stylus',
 'presuming',
 'gon',
 'bovary',
 'payday',
 'hauling',
 'bloodsucking',
 'bimbos',
 'ventilation',
 'ritualistically',
 'recommending',
 'inscription',
 'ejaculations',
 'hunnam',
 'hob',
 'dfroqu',
 'conservatory',
 'overreaching',
 'mannara',
 'disconnect',
 'claire',
 'stefania',
 'machettes',
 'brazlia',
 'perinal',
 'briget',
 'subtitle',
 'nebulous',
 'closures',
 'lurid',
 'welk',
 'parineeta',
 'bounds',
 'cem',
 'ofcourse',
 'prevailing',
 'objectiveness',
 'megan',
 'bernet',
 'nuthin',
 'prize',
 'cantina',
 'nott',
 'recite',
 'lionheart',
 'redub',
 'offscreen',
 'sensuous',
 'poultry',
 'frees',
 'crispian',
 'ruinously',
 'contribute',
 'sculpted',
 'timeless',
 'wwf',
 'sear',
 'traumatic',
 'spectacle',
 'historically',
 'triumphant',
 'geographic',
 'nirupa',
 'deconstructs',
 'disassociate',
 'scum',
 'abductor',
 'sum',
 'romolo',
 'creed',
 'shortfall',
 'diminutive',
 'kana',
 'ise',
 'rowsdower',
 'cuckoo',
 'outclasses',
 'somersaulting',
 'inartistic',
 'guesswork',
 'argeninean',
 'forties',
 'emotion',
 'discotheque',
 'horthy',
 'interwhined',
 'slav',
 'hdv',
 'rigger',
 'defies',
 'monica',
 'drago',
 'munching',
 'dozing',
 'trenches',
 'mesmerizingly',
 'contracts',
 'glances',
 'fiasco',
 'disneyish',
 'disloyal',
 'parapluies',
 'parveen',
 'punishment',
 'mrio',
 'crucial',
 'skedaddled',
 'selldal',
 'atrociousness',
 'rejoicing',
 'anus',
 'aggressively',
 'harkens',
 'bunches',
 'playrights',
 'cinemagic',
 'thugees',
 'athon',
 'irrepressible',
 'ooverall',
 'breathe',
 'waltz',
 'digested',
 'endless',
 'page',
 'recuperating',
 'deposited',
 'jarols',
 'recycler',
 'sprung',
 'repels',
 'ogar',
 'misbehaviour',
 'solaris',
 'coding',
 'syncrhronized',
 'allegiances',
 'bushwhackers',
 'constrains',
 'vulnerability',
 'frosty',
 'suite',
 'icegun',
 'unremarked',
 'muscleheads',
 'dimitriades',
 'storywriter',
 'paradoxical',
 'shatners',
 'governmentally',
 'reb',
 'thumbtacks',
 'polick',
 'frenetic',
 'meena',
 'belying',
 'waner',
 'chuke',
 'bolting',
 'basin',
 'aronofsky',
 'torre',
 'youthful',
 'reviews',
 'ranches',
 'distractedly',
 'gagne',
 'ashford',
 'overhyped',
 'sheltered',
 'mustangs',
 'calendar',
 'doubly',
 'dicing',
 'americn',
 'fainted',
 'batpeople',
 'freakery',
 'cures',
 'instituting',
 'kebab',
 'depression',
 'australian',
 'pooped',
 'crevice',
 'angelos',
 'mutinous',
 'therefore',
 'lilting',
 'answering',
 'unmercilessly',
 'enrapt',
 'ulfsak',
 'swarg',
 'etvorka',
 'kiyomasa',
 'zorak',
 'chegwin',
 'frogland',
 'gener',
 'clovis',
 'prescribed',
 'claudi',
 'steered',
 'acre',
 'verifies',
 'rub',
 'homoerotic',
 'sprinted',
 'telegraph',
 'paulo',
 'cryptozoology',
 'wallpapers',
 'temple',
 'lauderdale',
 'layered',
 'vernetta',
 'visualise',
 'oven',
 'sited',
 'yong',
 'plate',
 'validation',
 'executions',
 'natica',
 'amitabhz',
 'powerbombed',
 'astronishing',
 'movergoers',
 'constants',
 'adventist',
 'ttm',
 'pandora',
 'haggerty',
 'zano',
 'respondents',
 'amerindian',
 'sima',
 'dumbest',
 'purposefully',
 'hairstylist',
 'pish',
 'ahead',
 'fooling',
 'rooster',
 'vovchenko',
 'aimless',
 'saps',
 'canutt',
 'benches',
 'inciteful',
 'underage',
 'loki',
 'nosiest',
 'disallows',
 'inciting',
 'lance',
 'crass',
 'nookie',
 'gilford',
 'doofenshmirtz',
 'baron',
 'stating',
 'convulsions',
 'hiraizumi',
 'storming',
 'gungans',
 'hopton',
 'sincere',
 'taka',
 'morbuis',
 'fetishist',
 'himmel',
 'waded',
 'nonconformity',
 'everly',
 'nesting',
 'slashing',
 'winking',
 'racers',
 'emphatic',
 'echelon',
 'posse',
 'fifty',
 'whizzpopping',
 'brigadier',
 'huntsville',
 'plaintiffs',
 'bellevue',
 'uncinematic',
 'mull',
 'alimony',
 'nietzche',
 'blockade',
 'saterday',
 'villainy',
 'implausiblities',
 'brendon',
 'dixie',
 'foodie',
 'discordant',
 'sweep',
 'coughherculescough',
 'secs',
 'lapse',
 'intersection',
 'aikens',
 'apace',
 'unprocessed',
 'lancashire',
 'widower',
 'nunchucks',
 'augie',
 'nozires',
 'rousing',
 'thalassa',
 'zagreb',
 'spellbounding',
 'coverbox',
 'illustrative',
 'btk',
 'smalltalk',
 'aaaarrgh',
 'distinguishable',
 'concerts',
 'seamanship',
 'slops',
 'choppily',
 'bestowing',
 'stinger',
 'manchester',
 'canceled',
 'competences',
 'teegra',
 'allegation',
 'timpani',
 'oppurunity',
 'averback',
 'scrounge',
 'worser',
 'drool',
 'outsourced',
 'heat',
 'tribes',
 'katsu',
 'orenthal',
 'fountain',
 'robustly',
 'suge',
 'totally',
 'batteries',
 'smart',
 'fcker',
 'bouchemi',
 'dheeraj',
 'vulgarly',
 'bafta',
 'superimpose',
 'junctures',
 'outback',
 'wry',
 'scholastic',
 'apparantly',
 'contracter',
 'but',
 'fastened',
 'spetember',
 'eeks',
 'idiomatic',
 'encountering',
 'pup',
 'subverted',
 'blum',
 'bluer',
 'loafers',
 'eighteenth',
 'dodgeball',
 'joads',
 'slovenly',
 'uncompromising',
 'showed',
 'absurdness',
 'bernson',
 'verisimilitude',
 'intensify',
 'reconstruct',
 'synagogues',
 'diwali',
 'pours',
 'hypnotizing',
 'depress',
 'usage',
 'boyle',
 'letting',
 'contiguous',
 'renegade',
 'catalan',
 'irishmen',
 'nemisis',
 'peasantry',
 'dartmouth',
 'resilience',
 'jiminy',
 'forcefully',
 'erna',
 'unevenness',
 'significance',
 'pendanski',
 'dudley',
 'hateful',
 'reliably',
 'misgivings',
 'triviality',
 'krauss',
 'matts',
 'uncovers',
 'trainees',
 'median',
 'hardline',
 'rainfall',
 'anaesthesia',
 'computational',
 'consulted',
 'huff',
 'candace',
 'added',
 'flocking',
 'flings',
 'girldfriends',
 'commenters',
 'spires',
 'flatlands',
 'orbit',
 'devgans',
 'ricardo',
 'toured',
 'moan',
 'institution',
 'daniell',
 'misinformative',
 'eisenhower',
 'ihop',
 'foolhardiness',
 'dreaful',
 'straitjacket',
 'owww',
 'kerosine',
 'marsalis',
 'tvg',
 'documentalists',
 'huddled',
 'veal',
 'hock',
 'batrice',
 'wnat',
 'tit',
 'facility',
 'faylen',
 'confiscates',
 'unfold',
 'diem',
 'gywnne',
 'dmv',
 'westcourt',
 'motherland',
 'urbanized',
 'kilter',
 'tarot',
 'oater',
 'highschool',
 'relecting',
 'einer',
 'jihadists',
 'galton',
 'vocation',
 'violations',
 'brenna',
 'peretti',
 'expeditioners',
 'disgust',
 'speaksman',
 'wuhl',
 'businesspeople',
 'rampaged',
 'wreckage',
 'buchman',
 'joby',
 'bachstage',
 'decide',
 'schweibert',
 'halmark',
 'intension',
 'foregrounded',
 'jokester',
 'duhs',
 'felisberto',
 'boinking',
 'reassuringly',
 'fancifully',
 'hendler',
 'plebes',
 'hoggish',
 'niamh',
 'bullish',
 ...]

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,
 'rohauer': 1,
 'conclusive': 2,
 'linnea': 3,
 'stalked': 4,
 'ramya': 5,
 'twi': 6,
 'clche': 7,
 'darwininan': 8,
 'seigner': 9,
 'nrw': 10,
 'henrietta': 11,
 'roehler': 12,
 'impossibly': 13,
 'vhs': 14,
 'vets': 15,
 'jobyna': 16,
 'riead': 17,
 'disenchanted': 18,
 'unearthly': 19,
 'carrol': 20,
 'evy': 21,
 'livestock': 22,
 'misery': 23,
 'immigrating': 24,
 'talia': 25,
 'angeli': 26,
 'subor': 27,
 'piana': 28,
 'lidl': 29,
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 'confiscates': 954,
 'unfold': 955,
 'diem': 956,
 'gywnne': 957,
 'dmv': 958,
 'westcourt': 959,
 'motherland': 960,
 'urbanized': 961,
 'kilter': 962,
 'tarot': 963,
 'oater': 964,
 'highschool': 965,
 'relecting': 966,
 'einer': 967,
 'jihadists': 968,
 'galton': 969,
 'vocation': 970,
 'violations': 971,
 'brenna': 972,
 'peretti': 973,
 'expeditioners': 974,
 'disgust': 975,
 'speaksman': 976,
 'wuhl': 977,
 'businesspeople': 978,
 'rampaged': 979,
 'wreckage': 980,
 'buchman': 981,
 'joby': 982,
 'bachstage': 983,
 'decide': 984,
 'schweibert': 985,
 'halmark': 986,
 'intension': 987,
 'foregrounded': 988,
 'jokester': 989,
 'duhs': 990,
 'felisberto': 991,
 'boinking': 992,
 'reassuringly': 993,
 'fancifully': 994,
 'hendler': 995,
 'plebes': 996,
 'hoggish': 997,
 'niamh': 998,
 'bullish': 999,
 ...}

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 [31]:

    

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()):
                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 [ ]:

    

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


    

In [ ]:

    

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


    

In [ ]:

    

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


    

In [ ]:

    

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


    

In [ ]:

    

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


    

In [ ]:

    

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


    

In [ ]:

    

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


    

In [ ]:

    

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


    

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)]