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', 0.43872232986464682),
 ('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]:





['',
 'lupe',
 'bleibtreu',
 'industrious',
 'manges',
 'sanfrancisco',
 'dreamcast',
 'dysfuntional',
 'catholique',
 'hippolyte',
 'genuinely',
 'voucher',
 'stupednous',
 'macroscopic',
 'aunt',
 'balu',
 'string',
 'personified',
 'corpulent',
 'leat',
 'chap',
 'sistematski',
 'dawns',
 'generalities',
 'restrict',
 'winfrey',
 'instituting',
 'escreve',
 'canals',
 'rivet',
 'rose',
 'roxann',
 'cultivated',
 'hummers',
 'proclamation',
 'screecher',
 'limbo',
 'backer',
 'nekhron',
 'tit',
 'blueray',
 'registrar',
 'tripped',
 'dougray',
 'karima',
 'titillation',
 'anika',
 'buck',
 'hildy',
 'supervise',
 'unplugged',
 'trended',
 'skyrockets',
 'rearrange',
 'william',
 'flounced',
 'ger',
 'numerical',
 'castlebeck',
 'prabhats',
 'firms',
 'dearz',
 'constrains',
 'kimi',
 'lelouch',
 'cleveland',
 'mellon',
 'matondkar',
 'elektra',
 'gobbling',
 'mesa',
 'detained',
 'kaboud',
 'ali',
 'shrieks',
 'ithot',
 'borges',
 'specify',
 'dismaying',
 'sealing',
 'hugely',
 'socioty',
 'moolah',
 'threaten',
 'galley',
 'grasp',
 'whereby',
 'dumbstuck',
 'fervor',
 'formations',
 'suspenders',
 'rrhs',
 'forefather',
 'storymode',
 'campos',
 'katzelmacher',
 'philosophized',
 'politburo',
 'coopers',
 'whoosing',
 'dp',
 'sevencard',
 'johannesburg',
 'oliveira',
 'passer',
 'occupy',
 'pooling',
 'segment',
 'duping',
 'delineate',
 'reeeeeaally',
 'irrelevancy',
 'campsite',
 'backers',
 'emote',
 'psychotherapists',
 'trucking',
 'qi',
 'smurfettes',
 'woodie',
 'rgv',
 'glories',
 'music',
 'destines',
 'een',
 'crudity',
 'mosntres',
 'methodists',
 'bohemia',
 'stuccoed',
 'gel',
 'imprisoning',
 'sartana',
 'ut',
 'enfant',
 'downsides',
 'nozzle',
 'foop',
 'vow',
 'underwhelmed',
 'mungle',
 'amati',
 'reforging',
 'placido',
 'slayed',
 'aggh',
 'amillenialist',
 'surroundings',
 'sanest',
 'banally',
 'warbling',
 'leung',
 'oppressive',
 'carrols',
 'mastercard',
 'hedrin',
 'logically',
 'destroyers',
 'spades',
 'disagree',
 'bunce',
 'uproar',
 'atheism',
 'dinaggioi',
 'farscape',
 'asylums',
 'tritely',
 'klein',
 'commences',
 'urgency',
 'gretta',
 'jordi',
 'rickles',
 'garca',
 'hille',
 'duhllywood',
 'kundera',
 'rubberized',
 'murkier',
 'lightfoot',
 'roladex',
 'macmurray',
 'hamminess',
 'disneyish',
 'zyuranger',
 'snyapses',
 'introduction',
 'qualms',
 'grooms',
 'galaxies',
 'showy',
 'wallis',
 'bereaving',
 'launchpad',
 'momentary',
 'terminal',
 'subset',
 'trustworthiness',
 'nakamura',
 'elba',
 'ebenezer',
 'kookily',
 'atmosphere',
 'flaunting',
 'tapestries',
 'plum',
 'shaaadaaaap',
 'barrister',
 'disliked',
 'clavier',
 'hood',
 'lin',
 'veen',
 'occultist',
 'ruffalo',
 'filaments',
 'gs',
 'arent',
 'cabell',
 'causes',
 'eally',
 'maiko',
 'luske',
 'matchpoint',
 'oscillators',
 'bashers',
 'stupidily',
 'melania',
 'tessier',
 'solids',
 'sinclair',
 'ogles',
 'jack',
 'baaaaaaaaaaad',
 'ladiesman',
 'hinders',
 'constructive',
 'neverending',
 'scrabbles',
 'tor',
 'poem',
 'montag',
 'persuasive',
 'dechifered',
 'manjit',
 'illudere',
 'university',
 'graaf',
 'recruits',
 'nowhere',
 'scarecrow',
 'frulein',
 'flags',
 'cliche',
 'houses',
 'intramural',
 'phases',
 'hasty',
 'tinkly',
 'arduno',
 'massaccesi',
 'admitting',
 'waterfalls',
 'corben',
 'petroleum',
 'qc',
 'hangs',
 'roves',
 'acolytes',
 'abetted',
 'bruno',
 'validation',
 'heartbreak',
 'wickerman',
 'philedelphia',
 'shrift',
 'website',
 'kahlua',
 'webb',
 'evened',
 'munroe',
 'mormondom',
 'powerweight',
 'bratwurst',
 'heathen',
 'desu',
 'mccrea',
 'pterodactyls',
 'staphani',
 'hicksville',
 'caricature',
 'raj',
 'povich',
 'wo',
 'labyrinth',
 'fatcheek',
 'coyote',
 'expressway',
 'edina',
 'mankin',
 'rubrick',
 'maids',
 'fx',
 'pond',
 'dive',
 'transcendant',
 'strobing',
 'counterculture',
 'arsewit',
 'hotbed',
 'seediest',
 'class',
 'guile',
 'pollack',
 'divide',
 'trotting',
 'alltime',
 'seftel',
 'bushi',
 'saskatoon',
 'favre',
 'escapeuntil',
 'tortuously',
 'immeasurable',
 'filmation',
 'doig',
 'coulson',
 'dater',
 'taft',
 'wk',
 'pristine',
 'anbuchelvan',
 'nicco',
 'unbalances',
 'filthiness',
 'barbarism',
 'tableaux',
 'walston',
 'hillarious',
 'knockoff',
 'discplines',
 'massey',
 'mediation',
 'autobiographic',
 'geek',
 'boldt',
 'celled',
 'manfredini',
 'angrily',
 'drifting',
 'spouts',
 'europa',
 'faker',
 'links',
 'idolizes',
 'cates',
 'porcupine',
 'outreach',
 'pieczanski',
 'seond',
 'almoust',
 'explosives',
 'trannies',
 'homepages',
 'vlissingen',
 'ufern',
 'wing',
 'girardot',
 'sill',
 'ailed',
 'bolha',
 'fartsys',
 'givings',
 'underplays',
 'swarming',
 'andrea',
 'tonto',
 'disciple',
 'tila',
 'licensure',
 'odbray',
 'rowland',
 'chives',
 'gozu',
 'ronny',
 'hoarding',
 'unreachable',
 'shizophrenic',
 'mature',
 'undestand',
 'carafotes',
 'hanna',
 'sixty',
 'expeditioners',
 'haphazard',
 'shops',
 'acct',
 'heartpounding',
 'kailin',
 'dime',
 'crazes',
 'britain',
 'karma',
 'brutalizing',
 'espanol',
 'zaniness',
 'tampering',
 'wiser',
 'electrocute',
 'nastier',
 'journeying',
 'highjly',
 'adjustment',
 'lecturing',
 'tlb',
 'duke',
 'incurs',
 'pertwee',
 'denzell',
 'devastatingly',
 'vaude',
 'billion',
 'levi',
 'coop',
 'drippy',
 'accustomed',
 'cardella',
 'translated',
 'doubtfully',
 'debtors',
 'grue',
 'moviestar',
 'mellissa',
 'clinkers',
 'pudding',
 'scrooges',
 'setback',
 'ecologic',
 'rockabilly',
 'sonego',
 'affronting',
 'etvorka',
 'department',
 'hawas',
 'poker',
 'hangers',
 'chahine',
 'dereks',
 'unattuned',
 'illigal',
 'distanced',
 'raver',
 'wits',
 'homerian',
 'skank',
 'brings',
 'gem',
 'sketchy',
 'huddle',
 'blyth',
 'fly',
 'chequered',
 'anno',
 'flatness',
 'nomad',
 'thematics',
 'vigilant',
 'suprise',
 'instructions',
 'levine',
 '.',
 'dorkiest',
 'wrestlers',
 'patrons',
 'aphoristic',
 'despondency',
 'raubal',
 'setpiece',
 'suffers',
 'psychiatry',
 'nearby',
 'finding',
 'yippee',
 'purposeful',
 'kings',
 'moderator',
 'jaid',
 'undr',
 'oro',
 'saturate',
 'recognise',
 'prowls',
 'bypass',
 'surpassing',
 'characteriology',
 'toon',
 'matt',
 'bests',
 'northmen',
 'conjure',
 'jonesing',
 'savages',
 'warship',
 'revamp',
 'groupe',
 'powering',
 'tamest',
 'massacrenot',
 'creativity',
 'transmissions',
 'ornithologist',
 'kenovic',
 'terminatrix',
 'gander',
 'deplore',
 'indianapolis',
 'pavlov',
 'fresnay',
 'revealed',
 'railways',
 'marienbad',
 'greystone',
 'genndy',
 'unfocused',
 'pranked',
 'huit',
 'senegalese',
 'everingham',
 'lustreless',
 'bamboo',
 'decoff',
 'filmometer',
 'notebook',
 'yegg',
 'dressler',
 'manpower',
 'sods',
 'lulu',
 'unlicensed',
 'caucasian',
 'ghotst',
 'pavelic',
 'photowise',
 'ironside',
 'lifshitz',
 'sith',
 'reccomended',
 'interleave',
 'uncomprehensible',
 'hirsh',
 'stables',
 'enthrall',
 'capitulate',
 'submit',
 'flirtations',
 'kiva',
 'sara',
 'wah',
 'phycho',
 'carvalho',
 'affirm',
 'birthmother',
 'defensa',
 'forton',
 'dardis',
 'stereos',
 'wnk',
 'zzzzzzzzzzzz',
 'superwonderscope',
 'kurta',
 'soulless',
 'klebb',
 'dictatorial',
 'dizzying',
 'batouch',
 'sportswriter',
 'arrrghhhhhhs',
 'dcors',
 'loureno',
 'busybody',
 'oppikoppi',
 'exhooker',
 'pronounced',
 'conaughey',
 'icarus',
 'regained',
 'manjayegi',
 'broadened',
 'nashville',
 'capitalise',
 'narcissus',
 'surprisingly',
 'decry',
 'sturgeon',
 'madolyn',
 'admittance',
 'neweyes',
 'spectecular',
 'kaabee',
 'napping',
 'psychiatrist',
 'miscegenation',
 'diff',
 'prescott',
 'hypocritical',
 'lwr',
 'eeks',
 'lennier',
 'merman',
 'incapacitated',
 'strip',
 'practice',
 'untergang',
 'falwell',
 'adi',
 'ahahahahahaaaaa',
 'teen',
 'heroes',
 'reviled',
 'outrageous',
 'squatter',
 'manoj',
 'orlander',
 'sadahiv',
 'aleck',
 'honkytonks',
 'pulchritudinous',
 'harbored',
 'banquo',
 'dupes',
 'wedgie',
 'catholiques',
 'pedicab',
 'compensate',
 'popcorncoke',
 'sociopathy',
 'torme',
 'raged',
 'barely',
 'ib',
 'bethany',
 'foolishly',
 'deducts',
 'hennenlotter',
 'thence',
 'afterwhile',
 'negotiated',
 'readable',
 'hangar',
 'nationalists',
 'forces',
 'orville',
 'untangle',
 'curves',
 'skeptically',
 'snakebite',
 'logand',
 'constructor',
 'intensional',
 'superfun',
 'regime',
 'demagogue',
 'hafte',
 'argeninean',
 'strolls',
 'languages',
 'inbred',
 'adjusted',
 'bilge',
 'karmas',
 'chonopolisians',
 'piglet',
 'sebastiaans',
 'preiti',
 'bailout',
 'kazuhiro',
 'jaden',
 'revision',
 'shave',
 'hears',
 'dreamquest',
 'portaraying',
 'mannequin',
 'rayed',
 'incidentally',
 'dodds',
 'truax',
 'farmani',
 'headlining',
 'holman',
 'landholdings',
 'tomboy',
 'redemptions',
 'krusty',
 'girotti',
 'decoder',
 'amateuristic',
 'cellmates',
 'prostitutes',
 'pupart',
 'stubbed',
 'fairly',
 'sceptical',
 'suzannes',
 'eisley',
 'discontentment',
 'bergen',
 'overpopulation',
 'exeggcute',
 'wippleman',
 'keiko',
 'youngs',
 'jeanie',
 'duncan',
 'coixet',
 'misfit',
 'kurtz',
 'jimi',
 'frequents',
 'sharpen',
 'confide',
 'programmes',
 'watcheable',
 'barem',
 'lippmann',
 'socio',
 'burbling',
 'brulier',
 'glitchy',
 'newberry',
 'awareness',
 'craze',
 'rebuild',
 'destroy',
 'diet',
 'krug',
 'len',
 'dde',
 'umpf',
 'rotary',
 'sardine',
 'desaturate',
 'evanescence',
 'suggestion',
 'bohemian',
 'rehman',
 'osiric',
 'desperateness',
 'jerri',
 'apolitical',
 'understatedly',
 'sturla',
 'appendage',
 'coexisted',
 'ostracization',
 'meld',
 'mayan',
 'pillars',
 'syncopated',
 'makes',
 'eccentricity',
 'bestbut',
 'presidente',
 'apparantly',
 'hatching',
 'babson',
 'ossification',
 'tolerant',
 'dumbo',
 'nationalities',
 'vocalist',
 'arduously',
 'inexpensive',
 'backtrack',
 'ahahahahahhahahahahahahahahahhahahahahahahah',
 'estes',
 'expressiveness',
 'sunroof',
 'biographers',
 'lisps',
 'whiskey',
 'romaro',
 'expressively',
 'kevnjeff',
 'schizophrenia',
 'swapping',
 'marque',
 'southron',
 'myth',
 'smothered',
 'unrealness',
 'hoosiers',
 'mortis',
 'roadrunner',
 'cereals',
 'follywood',
 'shake',
 'iraqis',
 'outrageousness',
 'ply',
 'righto',
 'eduction',
 'mitch',
 'condescended',
 'sleek',
 'janelle',
 'flutters',
 'tk',
 'colette',
 'profuse',
 'grumbled',
 'ont',
 'tempts',
 'foothold',
 'ratcatcher',
 'brutish',
 'nipper',
 'sydney',
 'simulation',
 'bonnevie',
 'sedition',
 'gulliver',
 'filmhistory',
 'harnesses',
 'impressionists',
 'handshakes',
 'plopped',
 'ornaments',
 'lorri',
 'peepers',
 'balinese',
 'heaving',
 'tiglon',
 'frogballs',
 'meyerowitz',
 'leverage',
 'jacqui',
 'tractor',
 'selective',
 'sphincter',
 'warred',
 'pinfold',
 'rollan',
 'survey',
 'financially',
 'tipoff',
 'investment',
 'shredding',
 'wright',
 'edie',
 'paesan',
 'couric',
 'eurosleaze',
 'dahlia',
 'weathered',
 'yasumi',
 'feud',
 'brusquely',
 'castlevania',
 'metamoprhis',
 'soured',
 'naefe',
 'recalled',
 'weensy',
 'nighty',
 'patted',
 'menopuasal',
 'childs',
 'toucan',
 'remembers',
 'springit',
 'elfort',
 'mailed',
 'spud',
 'knockouts',
 'coherent',
 'ivan',
 'sitter',
 'poseidon',
 'influx',
 'escargot',
 'wealth',
 'goosier',
 'dereliction',
 'wada',
 'shys',
 'lawlessness',
 'waldeman',
 'blessedly',
 'couplings',
 'public',
 'ahhhhhh',
 'severin',
 'shopkeeper',
 'notches',
 'metaphorical',
 'peliky',
 'weihenmeyer',
 'poolguy',
 'ogi',
 'number',
 'reccomend',
 'herek',
 'pomerantz',
 'supersize',
 'cup',
 'stat',
 'fluid',
 'zasu',
 'antartic',
 'dass',
 'cobern',
 'transformers',
 'anglos',
 'bellamy',
 'teletype',
 'lessen',
 'fatherland',
 'appeased',
 'clambake',
 'yetians',
 'dweeb',
 'funicello',
 'cele',
 'km',
 'embroidering',
 'grayce',
 'tait',
 'neatly',
 'itinerant',
 'lack',
 'sheeks',
 'plainsman',
 'metamorphis',
 'zapar',
 'quoters',
 'romps',
 'mckee',
 'ingalls',
 'stubly',
 'tur',
 'eritated',
 'goodluck',
 'fanfilm',
 'matel',
 'barabar',
 'oy',
 'dini',
 'innsbruck',
 'charton',
 'clowes',
 'sportsmen',
 'predict',
 'kusakari',
 'lunceford',
 'kish',
 'fonterbras',
 'fruitfully',
 'behold',
 'splaining',
 'ordell',
 'definatey',
 'lve',
 'shapeshifter',
 'hairless',
 'inroads',
 'diabolic',
 'sloooow',
 'striesand',
 'aag',
 'siouxie',
 'unbelievable',
 'standards',
 'slugging',
 'barn',
 'repressive',
 'sportsmanship',
 'circulating',
 'duilio',
 'ravensbrck',
 'allowed',
 'jir',
 'tampax',
 'wabbits',
 'hindrance',
 'macau',
 'artificially',
 'innuendo',
 'boardwalk',
 'counterweight',
 'trainyard',
 'alos',
 'alejo',
 'anglicised',
 'unglued',
 'surmounting',
 'fruits',
 'climates',
 'blackmailing',
 'kittredge',
 ...]

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,
 'lupe': 1,
 'bleibtreu': 2,
 'industrious': 3,
 'manges': 4,
 'sanfrancisco': 5,
 'dreamcast': 6,
 'dysfuntional': 7,
 'catholique': 8,
 'hippolyte': 9,
 'genuinely': 10,
 'voucher': 11,
 'stupednous': 12,
 'macroscopic': 13,
 'aunt': 14,
 'balu': 15,
 'string': 16,
 'personified': 17,
 'corpulent': 18,
 'leat': 19,
 'chap': 20,
 'sistematski': 21,
 'dawns': 22,
 'generalities': 23,
 'restrict': 24,
 'winfrey': 25,
 'instituting': 26,
 'escreve': 27,
 'canals': 28,
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 'lunceford': 954,
 'kish': 955,
 'fonterbras': 956,
 'fruitfully': 957,
 'behold': 958,
 'splaining': 959,
 'ordell': 960,
 'definatey': 961,
 'lve': 962,
 'shapeshifter': 963,
 'hairless': 964,
 'inroads': 965,
 'diabolic': 966,
 'sloooow': 967,
 'striesand': 968,
 'aag': 969,
 'siouxie': 970,
 'unbelievable': 971,
 'standards': 972,
 'slugging': 973,
 'barn': 974,
 'repressive': 975,
 'sportsmanship': 976,
 'circulating': 977,
 'duilio': 978,
 'ravensbrck': 979,
 'allowed': 980,
 'jir': 981,
 'tampax': 982,
 'wabbits': 983,
 'hindrance': 984,
 'macau': 985,
 'artificially': 986,
 'innuendo': 987,
 'boardwalk': 988,
 'counterweight': 989,
 'trainyard': 990,
 'alos': 991,
 'alejo': 992,
 'anglicised': 993,
 'unglued': 994,
 'surmounting': 995,
 'fruits': 996,
 'climates': 997,
 'blackmailing': 998,
 'kittredge': 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 [27]:

    

import time, sys


    

In [2]:

    

class SentimentNetwork:
    def __init__(self, reviews, labels, hidden_nodes=10, learning_rate=0.1):
        
        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 = []
        for review in reviews:
            for word in review.split(' '):
                review_vocab.append(word)
        self.review_vocab = review_vocab
        
        label_vocab = []
        for label in labels:
            label_vocab.append(label)
        self.label_vocab = label_vocab
        
        self.review_vocab_size = len(review_vocab)
        self.label_vocab_size = len(label_vocab)
        
        self.word2index = {}
        for i, word in enumerate(vocab):
            self.word2index[word] = i
            
        self.label2index = {}
        for i, label in enumerate(vocab):
            self.label2index[label] = i
            
    def init_network(self, input_nodes, hidden_nodes, output_nodes, learning_rate):
        
        self.input_nodes = input_nodes
        self.hidden_nodes = hidden_nodes
        self.output_nodes = output_nodes
        
        self.weights_input_to_hidden = np.zeros((self.input_nodes, self.hidden_nodes))
        self.weights_hidden_to_output = 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]
            
            ### Foward pass ###
            # Input layer
            self.update_input_layer(review)
            
            # Hidden layer
            layer_1 = self.layer_0.dot(self.weights_input_to_hidden)
            
            # Output layer
            layer_2 = self.sigmoid(layer_1.dot(self.weights_hidden_to_output))
            
            ### Backward pass ###
            # Output error
            layer_2_error = self.get_target_for_label(label) - layer_2
            layer_2_delta = layer_2_error * self.sigmoid_output_2_derivative(layer_2)
            
            # Hidden error
            layer_1_error = layer_2_delta.dot(self.weights_hidden_to_output.T)
            layer_1_delta = layer_1_error # hidden layer gradients - no nonlinearity so it's the same as the error (?)
            
            ### Update weights ###
            self.weights_hidden_to_output += self.learning_rate * layer_1.T.dot(layer_2_delta)
            self.weights_input_to_hidden += self.learning_rate * self.layer_0.T.dot(layer_1_delta)
            
            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_input_to_hidden)
            
        # Output layer
        layer_2 = self.sigmoid(layer_1.dot(self.weights_hidden_to_output))
        
        if(layer_2[0] > 0.5):
            return "POSITIVE"
        else:
            return "NEGATIVE"


    

In [3]:

    

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


    

    




---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-3-db0c07a56840> in <module>()
----> 1 mlp = SentimentNetwork(reviews[:-1000],labels[:-1000], learning_rate=0.1)

<ipython-input-2-e556f707eeca> in __init__(self, reviews, labels, hidden_nodes, learning_rate)
      2     def __init__(self, reviews, labels, hidden_nodes=10, learning_rate=0.1):
      3 
----> 4         np.random.seed(1)
      5 
      6         self.pre_process_data(reviews, labels)

NameError: name 'np' is not defined

In [30]:

    

mlp.test(reviews[-1000:],labels[-1000:])


    

    




Progress:99.9% Speed(reviews/sec):33.96% #Correct:500 #Tested:1000 Testing Accuracy:50.0%

In [ ]:

    

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


    

    




Progress:0.0% Speed(reviews/sec):0.0 #Correct:0 #Trained:1 Training Accuracy:0.0%

In [ ]: