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]:





['',
 'cort',
 'technicians',
 'nugget',
 'kurdish',
 'heron',
 'egoism',
 'adjacent',
 'enbom',
 'thigh',
 'shelves',
 'tended',
 'disable',
 'lustreless',
 'recurring',
 'illuminated',
 'prudent',
 'romany',
 'folksy',
 'stapelton',
 'pacific',
 'insulated',
 'facilty',
 'inchon',
 'idjits',
 'harrar',
 'myron',
 'shity',
 'ardala',
 'signposting',
 'birtwhistle',
 'moocow',
 'maximally',
 'mildread',
 'simpson',
 'wuss',
 'mulrony',
 'contradictorily',
 'shuffle',
 'mutilated',
 'lurches',
 'bartley',
 'sartorius',
 'gogool',
 'mcaffee',
 'talosian',
 'closest',
 'tos',
 'sanechaos',
 'bassis',
 'isolating',
 'whalin',
 'pter',
 'dil',
 'camp',
 'cobblestones',
 'kasadya',
 'girard',
 'theosophy',
 'malicious',
 'vieques',
 'crimefighting',
 'satanist',
 'normalizing',
 'shrunken',
 'compulsive',
 'auntie',
 'entrapment',
 'jz',
 'fazes',
 'jha',
 'conjunction',
 'kolbe',
 'yousef',
 'gainfully',
 'ozric',
 'phillipenes',
 'salutary',
 'ambrose',
 'cultism',
 'tortilla',
 'camui',
 'megapack',
 'zx',
 'idealistic',
 'pacts',
 'mirroed',
 'burning',
 'naivet',
 'splices',
 'hilcox',
 'parroting',
 'utlimately',
 'aaww',
 'counselled',
 'shillings',
 'friendkin',
 'increments',
 'mcnairy',
 'blaylock',
 'verbalizations',
 'sketch',
 'jensen',
 'toulon',
 'ny',
 'pyare',
 'retrospect',
 'nitu',
 'linklater',
 'danish',
 'serie',
 'gasses',
 'obscenity',
 'bvds',
 'tanger',
 'lanquage',
 'eulogies',
 'understated',
 'mascara',
 'pest',
 'mazes',
 'bijomaru',
 'resolutions',
 'bluff',
 'stemmed',
 'olajima',
 'claudio',
 'products',
 'dungy',
 'jeffries',
 'reacquainted',
 'rodriguez',
 'hue',
 'shepherds',
 'lockjaw',
 'ace',
 'baaaaaad',
 'smiley',
 'sleestak',
 'recherche',
 'moisture',
 'bible',
 'aptitude',
 'orbitting',
 'unconsumated',
 'kak',
 'egoistic',
 'johan',
 'ustashe',
 'wearily',
 'exceeds',
 'backstreets',
 'recites',
 'londoner',
 'siting',
 'readies',
 'rossen',
 'laboriously',
 'fuelling',
 'equalizer',
 'doled',
 'singe',
 'daniell',
 'tite',
 'arbiter',
 'famous',
 'bleakest',
 'fume',
 'pacifying',
 'wields',
 'misstep',
 'pols',
 'grierson',
 'teasingly',
 'disappointingly',
 'symmetric',
 'hosts',
 'jewell',
 'sesilia',
 'oskorblyonnye',
 'marcuse',
 'fiona',
 'geki',
 'irreverant',
 'plane',
 'overgrown',
 'airbag',
 'fightfest',
 'them',
 'nearne',
 'mendez',
 'lederhosen',
 'apocalypto',
 'refuses',
 'levity',
 'markell',
 'listing',
 'nighwatch',
 'thembrians',
 'hessians',
 'iritf',
 'diplomatic',
 'mutually',
 'chun',
 'blag',
 'butts',
 'electrified',
 'petersburg',
 'rex',
 'sugest',
 'notables',
 'greyhound',
 'stoked',
 'hoboken',
 'prettiest',
 'pulsating',
 'marched',
 'walmington',
 'borrowed',
 'caiman',
 'civilizations',
 'arsehole',
 'resettled',
 'toffs',
 'pakistani',
 'rocked',
 'bennifer',
 'reay',
 'lion',
 'donnell',
 'militaristic',
 'minimum',
 'yearned',
 'noodling',
 'cheetos',
 'betacam',
 'corder',
 'all',
 'darlian',
 'blindpassasjer',
 'nutjobseen',
 'google',
 'dishwater',
 'okerland',
 'supplements',
 'mcgee',
 'blind',
 'vert',
 'chainsaws',
 'abkani',
 'livinston',
 'forecaster',
 'jewels',
 'reputed',
 'olen',
 'hbc',
 'moot',
 'assumptions',
 'queue',
 'arron',
 'brusk',
 'losses',
 'dancers',
 'maze',
 'singlet',
 'tightest',
 'forsyte',
 'snl',
 'department',
 'paganism',
 'yali',
 'oakland',
 'ostracization',
 'ger',
 'protecting',
 'rainbows',
 'bobbitt',
 'hit',
 'tank',
 'knowingis',
 'gaskets',
 'maguires',
 'doggedly',
 'planetscapes',
 'ferdinandvongalitzien',
 'viewpoint',
 'befriend',
 'frontieres',
 'excited',
 'suplexing',
 'parsing',
 'presson',
 'necrophiliac',
 'ulfsak',
 'glady',
 'adeptly',
 'uglying',
 'superpowerman',
 'artifices',
 'yds',
 'plugged',
 'waiting',
 'apotheosis',
 'rabidly',
 'verma',
 'unfortuneatly',
 'solipsism',
 'submerges',
 'parlaying',
 'scud',
 'monicas',
 'ithe',
 'gimmickry',
 'crude',
 'zionist',
 'kostner',
 'syd',
 'doctoress',
 'bensonhurst',
 'vxzptdtphwdm',
 'marley',
 'cattlemen',
 'nitpick',
 'flagellistic',
 'soundgarden',
 'stretchy',
 'italianness',
 'imbues',
 'hispanic',
 'outlands',
 'destroys',
 'bloodiest',
 'metafiction',
 'brigid',
 'hangdog',
 'gloatingly',
 'memorably',
 'biggs',
 'capita',
 'eisenmann',
 'babban',
 'jace',
 'masterstroke',
 'holst',
 'numbering',
 'wannabe',
 'gaining',
 'clearance',
 'message',
 'dramatizing',
 'fun',
 'langford',
 'extraneous',
 'accost',
 'productive',
 'spiralling',
 'snuck',
 'savour',
 'faraday',
 'poseidon',
 'pomegranate',
 'airial',
 'reaaaaallly',
 'mrquez',
 'iraquis',
 'foreshadows',
 'kwami',
 'assaulters',
 'stinkers',
 'cannibalised',
 'galley',
 'facinating',
 'emptiveness',
 'aleya',
 'ripple',
 'thrill',
 'dobie',
 'jock',
 'although',
 'defend',
 'conceives',
 'unpleasantly',
 'hastens',
 'reestablish',
 'warmth',
 'yeshua',
 'shapeshifting',
 'bierce',
 'perp',
 'zelig',
 'crypts',
 'slight',
 'delphine',
 'characther',
 'guiseppe',
 'chilled',
 'loeb',
 'serenity',
 'niggling',
 'defecated',
 'gilley',
 'dissociative',
 'overwhelmingly',
 'coincidence',
 'lotta',
 'obliterate',
 'giuliani',
 'counterproductive',
 'drewitt',
 'rebuke',
 'bores',
 'moeurs',
 'clickety',
 'cameroun',
 'bestow',
 'lili',
 'ohtsji',
 'garibaldi',
 'amassed',
 'wwe',
 'bas',
 'ostracized',
 'ranching',
 'broke',
 'meanspirited',
 'rungs',
 'income',
 'condescend',
 'secluded',
 'brawled',
 'safarova',
 'fingerprinting',
 'lighten',
 'mommas',
 'cigliutti',
 'receive',
 'helium',
 'maro',
 'needed',
 'blossom',
 'exeggcute',
 'balding',
 'rosenstrae',
 'hander',
 'bri',
 'quirk',
 'interspersed',
 'porno',
 'symptoms',
 'subsistence',
 'laurdale',
 'celeste',
 'bogard',
 'dotes',
 'maternal',
 'witticisms',
 'catastrophe',
 'manichaean',
 'cha',
 'unfold',
 'annoyingly',
 'accolade',
 'unnaturally',
 'chopped',
 'falwell',
 'installments',
 'rejuvenation',
 'animals',
 'cliffhangin',
 'blackmailed',
 'elliott',
 'robowar',
 'icf',
 'quayle',
 'granger',
 'unfolds',
 'nothan',
 'descendents',
 'cumentery',
 'cohort',
 'precisely',
 'phenomenon',
 'okiyas',
 'befittingly',
 'spearheads',
 'chapelle',
 'ricci',
 'dent',
 'microbiology',
 'wagner',
 'napoleonic',
 'decaf',
 'sphincter',
 'natashia',
 'sneakiness',
 'pianist',
 'karns',
 'punctuation',
 'perfectness',
 'roshan',
 'executioner',
 'sasural',
 'darian',
 'superthunderstingcar',
 'motorbikes',
 'thuy',
 'shriveling',
 'outriders',
 'dnouement',
 'altamont',
 'reunion',
 'cavegirl',
 'carerra',
 'pseudocomedies',
 'drainage',
 'whoah',
 'vertido',
 'fridrik',
 'hk',
 'stripe',
 'icebox',
 'modicum',
 'deosnt',
 'tattoine',
 'vinson',
 'lilt',
 'hypes',
 'hemo',
 'nurturer',
 'crusoe',
 'rectal',
 'vacated',
 'almodvar',
 'stonewashed',
 'operate',
 'particles',
 'dedications',
 'geology',
 'epilogue',
 'clamour',
 'creepshow',
 'mcdonnel',
 'wast',
 'embrace',
 'demean',
 'yiddish',
 'priests',
 'cushions',
 'chaps',
 'carico',
 'hendrix',
 'helicopter',
 'loosely',
 'sepoys',
 'incantations',
 'somesuch',
 'grumbling',
 'estevo',
 'eroding',
 'barjatyas',
 'base',
 'fanny',
 'boogeman',
 'walpurgis',
 'meagan',
 'blaze',
 'tilmac',
 'flattered',
 'stultified',
 'palminterri',
 'grader',
 'notwithstanding',
 'bubble',
 'autocracy',
 'anton',
 'anja',
 'wraparound',
 'unprotected',
 'unmedicated',
 'hoyo',
 'rural',
 'conceding',
 'bleeds',
 'sympathy',
 'screed',
 'ees',
 'hensley',
 'suggest',
 'digressing',
 'silliest',
 'beleaguered',
 'meals',
 'ebon',
 'flamin',
 'francis',
 'sloppish',
 'matkondar',
 'croquet',
 'continental',
 'bleeping',
 'daly',
 'unshaven',
 'birthmark',
 'transposing',
 'vandicholai',
 'prevarications',
 'elefant',
 'summers',
 'hayle',
 'pressberger',
 'bushco',
 'potboiler',
 'secondus',
 'burgermister',
 'rei',
 'prichard',
 'thlema',
 'afterwards',
 'curitz',
 'villainy',
 'hangs',
 'apocalyptically',
 'olympic',
 'desperados',
 'extase',
 'incentives',
 'decreed',
 'nrnberg',
 'panhandle',
 'slathered',
 'purgatori',
 'publicize',
 'yakitate',
 'unredeemably',
 'dissemination',
 'marilee',
 'viennese',
 'commonsense',
 'editions',
 'frikkin',
 'requiem',
 'mhatre',
 'mashall',
 'commendations',
 'anything',
 'commentaries',
 'great',
 'dramatists',
 'bled',
 'huntsville',
 'generalities',
 'messick',
 'jigen',
 'caminho',
 'miller',
 'halarity',
 'lectured',
 'refused',
 'inferiors',
 'guarontee',
 'galligan',
 'tallman',
 'bucketfuls',
 'induces',
 'individualism',
 'lacing',
 'wasim',
 'empahsise',
 'pleased',
 'darkwing',
 'thrift',
 'frisk',
 'table',
 'wont',
 'helga',
 'irrefutable',
 'posher',
 'conduct',
 'beatlemaniac',
 'dissabordinate',
 'comedically',
 'munroe',
 'gibney',
 'undying',
 'circular',
 'exemplar',
 'contingency',
 'projectionist',
 'essay',
 'chancho',
 'succumbs',
 'dopplebangers',
 'folder',
 'editorial',
 'cuddles',
 'ayacoatl',
 'announces',
 'munnera',
 'warpaint',
 'ummmm',
 'tingled',
 'tony',
 'tht',
 'rodeos',
 'machaty',
 'enforces',
 'muito',
 'chainguns',
 'entwine',
 'harped',
 'flawed',
 'interfaith',
 'bracket',
 'cling',
 'xiv',
 'arcand',
 'brassieres',
 'glanse',
 'restating',
 'candyelise',
 'weaving',
 'quinn',
 'ditto',
 'winked',
 'moderated',
 'contemplate',
 'unbelievability',
 'detail',
 'dunsky',
 'glistening',
 'grope',
 'maru',
 'ibrahim',
 'martains',
 'film',
 'who',
 'include',
 'cuts',
 'faced',
 'witchhunt',
 'archambault',
 'shin',
 'koontz',
 'gungan',
 'streetfighters',
 'willingham',
 'oppressiveness',
 'clarifies',
 'manuscript',
 'nec',
 'landscapes',
 'dabrova',
 'parlors',
 'generation',
 'actresses',
 'steinauer',
 'staring',
 'uncompelling',
 'cattivi',
 'endings',
 'wooohooo',
 'raptus',
 'finney',
 'roomful',
 'japanese',
 'six',
 'ancillary',
 'riiiiiiight',
 'martian',
 'modernizing',
 'gasgoine',
 'reptilian',
 'acrimonious',
 'mushrooms',
 'fckin',
 'glorious',
 'transplant',
 'elope',
 'dopes',
 'misfiring',
 'dollys',
 'wilkinson',
 'doorstop',
 'bastedo',
 'artimisia',
 'prochnow',
 'assassino',
 'mogul',
 'disturbing',
 'flaccid',
 'meddings',
 'blanc',
 'thety',
 'benefactors',
 'automakers',
 'alecky',
 'indications',
 'enunciates',
 'slater',
 'pallet',
 'breastfeeding',
 'suriyothai',
 'lettieri',
 'misbehaving',
 'hemlock',
 'sequence',
 'pharmacy',
 'jordi',
 'decide',
 'dumbly',
 'delmar',
 'scylla',
 'outcome',
 'light',
 'darwell',
 'selves',
 'scrambling',
 'climbing',
 'cinemas',
 'afganistan',
 'centres',
 'walerian',
 'implanting',
 'heywood',
 'sedate',
 'bunches',
 'ayn',
 'councellor',
 'brechtian',
 'gary',
 'coyle',
 'swathed',
 'bemoans',
 'primer',
 'wada',
 'hearen',
 'caca',
 'avocado',
 'probibly',
 'charming',
 'ultramodern',
 'thingy',
 'madigan',
 'rarefied',
 'renowned',
 'berrisford',
 'splashes',
 'definaetly',
 'father',
 'vaugn',
 'bodysuit',
 'manuals',
 'universalised',
 'eo',
 'mag',
 'hyperventilating',
 'captivity',
 'morvern',
 'bods',
 'onset',
 'greengrass',
 'philosophies',
 'coyote',
 'hots',
 'dell',
 'ahab',
 'diplomacy',
 'superegos',
 'steeleye',
 'runyonesque',
 'piercings',
 'crewed',
 'forceful',
 'arched',
 'classics',
 'insincere',
 'unprovoked',
 'distinguishing',
 'bubbly',
 'shara',
 'stacie',
 'norma',
 'nimbus',
 'japes',
 'stimulates',
 'manslaughter',
 'effected',
 'autobots',
 'catacombs',
 'alerted',
 'mysterio',
 'menendez',
 'ikey',
 'clifford',
 'divorces',
 'verona',
 'cock',
 'handing',
 'naffness',
 'galvanize',
 'crout',
 'joining',
 'bernicio',
 'mcnally',
 'karyo',
 'pretended',
 'diners',
 'childlish',
 'dividing',
 'khamini',
 'snagged',
 'benefit',
 'bruce',
 'kelemen',
 'intercuts',
 'scan',
 'elicit',
 'contaminates',
 'zulus',
 'withering',
 'disregards',
 'tonk',
 'richandson',
 'abortion',
 'slower',
 'qu',
 'stairway',
 'fdny',
 'implausiblities',
 'cu',
 'necessary',
 'shaking',
 'replicator',
 'inanity',
 'miffed',
 'garp',
 'sudio',
 'texturing',
 'cratchitt',
 'siege',
 'daena',
 'waistband',
 'towner',
 'discernment',
 'polka',
 'drek',
 'worthiness',
 'peanut',
 'conservationists',
 'librarians',
 'sexshooter',
 'holland',
 'nullifying',
 'meda',
 'strenghtens',
 'winnie',
 'blonde',
 'righteous',
 'psmith',
 'palavras',
 'quada',
 'bluer',
 'chiba',
 'burrowing',
 'reassurance',
 'dinosaurus',
 'liam',
 'sexploitational',
 'runnin',
 'gijn',
 'elams',
 'armaments',
 'fercryinoutloud',
 'oceanography',
 'iconoclastic',
 'mainsequence',
 'picker',
 'intellectualized',
 'monopolist',
 'whorde',
 'sticked',
 'panegyric',
 'damns',
 'buries',
 'tailed',
 'leaping',
 'moden',
 'flic',
 'benny',
 'piso',
 'sidekicks',
 'imps',
 'seibert',
 'weis',
 'puertoricans',
 'addy',
 'vilarasau',
 'everlovin',
 'similar',
 'hawker',
 ...]

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,
 'cort': 1,
 'technicians': 2,
 'nugget': 3,
 'kurdish': 4,
 'heron': 5,
 'egoism': 6,
 'adjacent': 7,
 'enbom': 8,
 'thigh': 9,
 'shelves': 10,
 'tended': 11,
 'disable': 12,
 'lustreless': 13,
 'recurring': 14,
 'illuminated': 15,
 'prudent': 16,
 'romany': 17,
 'folksy': 18,
 'stapelton': 19,
 'pacific': 20,
 'insulated': 21,
 'facilty': 22,
 'inchon': 23,
 'idjits': 24,
 'harrar': 25,
 'myron': 26,
 'shity': 27,
 'ardala': 28,
 'signposting': 29,
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 'strenghtens': 954,
 'winnie': 955,
 'blonde': 956,
 'righteous': 957,
 'psmith': 958,
 'palavras': 959,
 'quada': 960,
 'bluer': 961,
 'chiba': 962,
 'burrowing': 963,
 'reassurance': 964,
 'dinosaurus': 965,
 'liam': 966,
 'sexploitational': 967,
 'runnin': 968,
 'gijn': 969,
 'elams': 970,
 'armaments': 971,
 'fercryinoutloud': 972,
 'oceanography': 973,
 'iconoclastic': 974,
 'mainsequence': 975,
 'picker': 976,
 'intellectualized': 977,
 'monopolist': 978,
 'whorde': 979,
 'sticked': 980,
 'panegyric': 981,
 'damns': 982,
 'buries': 983,
 'tailed': 984,
 'leaping': 985,
 'moden': 986,
 'flic': 987,
 'benny': 988,
 'piso': 989,
 'sidekicks': 990,
 'imps': 991,
 'seibert': 992,
 'weis': 993,
 'puertoricans': 994,
 'addy': 995,
 'vilarasau': 996,
 'everlovin': 997,
 'similar': 998,
 'hawker': 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
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 [28]:

    

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


    

In [29]:

    

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


    

    




Progress:99.9% Speed(reviews/sec):567.7% #Correct:500 #Tested:1000 Testing Accuracy:50.0%

In [30]:

    

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


    

    




Progress:0.0% Speed(reviews/sec):0.0 #Correct:0 #Trained:1 Training Accuracy:0.0%
Progress:10.4% Speed(reviews/sec):98.25 #Correct:1250 #Trained:2501 Training Accuracy:49.9%
Progress:20.8% Speed(reviews/sec):93.18 #Correct:2500 #Trained:5001 Training Accuracy:49.9%
Progress:31.2% Speed(reviews/sec):95.35 #Correct:3750 #Trained:7501 Training Accuracy:49.9%
Progress:41.6% Speed(reviews/sec):96.91 #Correct:5000 #Trained:10001 Training Accuracy:49.9%
Progress:52.0% Speed(reviews/sec):97.80 #Correct:6250 #Trained:12501 Training Accuracy:49.9%
Progress:62.5% Speed(reviews/sec):98.47 #Correct:7500 #Trained:15001 Training Accuracy:49.9%
Progress:72.9% Speed(reviews/sec):98.80 #Correct:8750 #Trained:17501 Training Accuracy:49.9%
Progress:83.3% Speed(reviews/sec):98.58 #Correct:10000 #Trained:20001 Training Accuracy:49.9%
Progress:93.7% Speed(reviews/sec):98.85 #Correct:11250 #Trained:22501 Training Accuracy:49.9%
Progress:99.9% Speed(reviews/sec):98.91 #Correct:11999 #Trained:24000 Training Accuracy:49.9%

In [31]:

    

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


    

In [32]:

    

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


    

    




Progress:0.0% Speed(reviews/sec):0.0 #Correct:0 #Trained:1 Training Accuracy:0.0%
Progress:10.4% Speed(reviews/sec):142.7 #Correct:1247 #Trained:2501 Training Accuracy:49.8%
Progress:20.8% Speed(reviews/sec):148.8 #Correct:2497 #Trained:5001 Training Accuracy:49.9%
Progress:31.2% Speed(reviews/sec):152.3 #Correct:3747 #Trained:7501 Training Accuracy:49.9%
Progress:41.6% Speed(reviews/sec):153.8 #Correct:4997 #Trained:10001 Training Accuracy:49.9%
Progress:52.0% Speed(reviews/sec):155.0 #Correct:6247 #Trained:12501 Training Accuracy:49.9%
Progress:62.5% Speed(reviews/sec):153.8 #Correct:7485 #Trained:15001 Training Accuracy:49.8%
Progress:72.9% Speed(reviews/sec):154.6 #Correct:8735 #Trained:17501 Training Accuracy:49.9%
Progress:83.3% Speed(reviews/sec):155.0 #Correct:9984 #Trained:20001 Training Accuracy:49.9%
Progress:93.7% Speed(reviews/sec):155.5 #Correct:11234 #Trained:22501 Training Accuracy:49.9%
Progress:99.9% Speed(reviews/sec):155.7 #Correct:11983 #Trained:24000 Training Accuracy:49.9%

In [33]:

    

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


    

In [34]:

    

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


    

    




Progress:0.0% Speed(reviews/sec):0.0 #Correct:0 #Trained:1 Training Accuracy:0.0%
Progress:10.4% Speed(reviews/sec):99.23 #Correct:1247 #Trained:2501 Training Accuracy:49.8%
Progress:20.8% Speed(reviews/sec):100.0 #Correct:2535 #Trained:5001 Training Accuracy:50.6%
Progress:31.2% Speed(reviews/sec):100.3 #Correct:3895 #Trained:7501 Training Accuracy:51.9%
Progress:41.6% Speed(reviews/sec):100.5 #Correct:5398 #Trained:10001 Training Accuracy:53.9%
Progress:52.0% Speed(reviews/sec):100.6 #Correct:6933 #Trained:12501 Training Accuracy:55.4%
Progress:62.5% Speed(reviews/sec):100.5 #Correct:8512 #Trained:15001 Training Accuracy:56.7%
Progress:72.9% Speed(reviews/sec):100.6 #Correct:10118 #Trained:17501 Training Accuracy:57.8%
Progress:83.3% Speed(reviews/sec):100.6 #Correct:11767 #Trained:20001 Training Accuracy:58.8%
Progress:93.7% Speed(reviews/sec):100.6 #Correct:13500 #Trained:22501 Training Accuracy:59.9%
Progress:99.9% Speed(reviews/sec):100.6 #Correct:14545 #Trained:24000 Training Accuracy:60.6%

In [ ]: