In [7]:

    

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 [8]:

    

len(reviews)


    

    
Out[8]:





25000

In [9]:

    

reviews[0]


    

    
Out[9]:





'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 [10]:

    

labels[0]


    

    
Out[10]:





'POSITIVE'

In [11]:

    

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 [12]:

    

from collections import Counter
import numpy as np


    

In [13]:

    

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


    

In [14]:

    

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 [15]:

    

positive_counts.most_common()


    

    
Out[15]:





[('', 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 [16]:

    

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 [17]:

    

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


    

    
Out[17]:





[('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),
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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),
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 ('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),
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 ('carrie', 1.076139432816051),
 ('blend', 1.0753554265038423),
 ('hank', 1.0736109864626924),
 ('diana', 1.0726368022648489),
 ('adorable', 1.0726368022648489),
 ('unexpected', 1.0722255334949147),
 ('achievement', 1.0668635903535293),
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 ('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),
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 ('hal', 0.92953595862417571),
 ('louis', 0.92871325187271225),
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 ('italy', 0.91629073187415511),
 ('gradually', 0.91629073187415511),
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 ('spy', 0.91289514287301687),
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 ('searching', 0.4393666597838457),
 ('rap', 0.43891304217570443),
 ('light', 0.43884433018199892),
 ('elizabeth', 0.43872232986464677),
 ('marry', 0.43861731542506488),
 ('learned', 0.43825493093115531),
 ('controversial', 0.43825493093115531),
 ('oz', 0.43825493093115531),
 ('slowly', 0.43785660389939979),
 ('comedic', 0.43721380642274466),
 ('wayne', 0.43721380642274466),
 ('thrilling', 0.43721380642274466),
 ('bridge', 0.43721380642274466),
 ('married', 0.43658501682196887),
 ('nazi', 0.4361020775700542),
 ('murder', 0.4353180712578455),
 ('physical', 0.4353180712578455),
 ('johnny', 0.43483971678806865),
 ('michelle', 0.43445264498141672),
 ('wallace', 0.43403848055222038),
 ('comedies', 0.43395706390247063),
 ('silent', 0.43395706390247063),
 ('played', 0.43387244114515305),
 ('international', 0.43363598507486073),
 ('vision', 0.43286408229627887),
 ('intelligent', 0.43196704885367099),
 ('shop', 0.43078291609245434),
 ('also', 0.43036720209769169),
 ('levels', 0.4302451371066513),
 ('miss', 0.43006426712153217),
 ('movement', 0.4295626596872249),
 ...]

In [18]:

    

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


    

    
Out[18]:





[('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 [19]:

    

from IPython.display import Image

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

Image(filename='sentiment_network.png')


    

    
Out[19]:

In [20]:

    

review = "The movie was excellent"

Image(filename='sentiment_network_pos.png')


    

    
Out[20]:

In [21]:

    

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


    

    




74074

In [22]:

    

list(vocab)


    

    
Out[22]:





['',
 'cockfighting',
 'companion',
 'metaphorically',
 'bureaucrats',
 'lollies',
 'cannabis',
 'portraited',
 'zones',
 'mcliam',
 'goldustluna',
 'voices',
 'vigilant',
 'supes',
 'madeasuck',
 'hbo',
 'trys',
 'soundly',
 'airways',
 'fintail',
 'munched',
 'pointe',
 'meal',
 'carver',
 'hoops',
 'dazzlingly',
 'capitalist',
 'testicle',
 'karogi',
 'stifle',
 'temples',
 'suspicious',
 'reigert',
 'indifferently',
 'percussion',
 'wheelchair',
 'intransigent',
 'superpowerman',
 'rantzen',
 'mccheese',
 'dithered',
 'sailors',
 'copycats',
 'immobile',
 'asha',
 'otami',
 'emails',
 'unchained',
 'meets',
 'pisa',
 'levelled',
 'trude',
 'slideshow',
 'tehran',
 'interweaving',
 'dice',
 'videodrome',
 'overturning',
 'participates',
 'unknowledgeable',
 'putative',
 'lowest',
 'maura',
 'jaques',
 'pandering',
 'languished',
 'outsource',
 'equip',
 'inquisition',
 'immoderate',
 'passively',
 'comment',
 'nexus',
 'reproaches',
 'shakespeareans',
 'heighten',
 'leg',
 'nokitofa',
 'mikl',
 'ekstase',
 'scoffed',
 'els',
 'slack',
 'fulcis',
 'yu',
 'maidens',
 'madeira',
 'pineyro',
 'serra',
 'repetoir',
 'spiro',
 'abducting',
 'particle',
 'dutt',
 'pacifical',
 'paradiso',
 'beeped',
 'yevgeni',
 'costal',
 'boonies',
 'wrung',
 'enraptured',
 'critised',
 'skosh',
 'concern',
 'amoretti',
 'demure',
 'carrillo',
 'decimates',
 'authorizes',
 'formalist',
 'wholes',
 'corpsethe',
 'yalom',
 'emand',
 'mcintyre',
 'implores',
 'eddington',
 'library',
 'basics',
 'drunken',
 'dividing',
 'divas',
 'crucifies',
 'compasses',
 'outreach',
 'scaffoldings',
 'pygmy',
 'ankylosaur',
 'kriemshild',
 'unthreatening',
 'rustlers',
 'frothing',
 'progrmmer',
 'invokes',
 'race',
 'effluvia',
 'ruts',
 'mammet',
 'cds',
 'waddlesworth',
 'trivializes',
 'brewing',
 'everyway',
 'recasted',
 'cheezie',
 'bwitch',
 'equilibrium',
 'raffs',
 'implements',
 'commishioner',
 'toliet',
 'kettle',
 'ciochetti',
 'semantic',
 'hoky',
 'furiouscough',
 'arturo',
 'guiry',
 'chiaroscuro',
 'bop',
 'irreversable',
 'reelers',
 'wired',
 'schizophrenic',
 'differing',
 'pretentions',
 'conceiving',
 'osenniy',
 'accumulation',
 'oopps',
 'exhaust',
 'necessities',
 'scotched',
 'ohh',
 'fondas',
 'toughen',
 'coattails',
 'enunciates',
 'turbans',
 'kwouk',
 'animates',
 'roms',
 'asssociated',
 'schygula',
 'beautify',
 'farells',
 'farce',
 'reopen',
 'rodney',
 'traversing',
 'sky',
 'sportsmanship',
 'ripe',
 'pata',
 'changeover',
 'elaine',
 'castlevania',
 'aggressiveness',
 'unpremeditated',
 'icarus',
 'y',
 'suggestions',
 'sheeple',
 'defends',
 'survivable',
 'bludgeoned',
 'somme',
 'muslim',
 'montano',
 'helix',
 'discharged',
 'creamed',
 'cr',
 'nuel',
 'mobarak',
 'hohokam',
 'adrienne',
 'salvific',
 'amoured',
 'ramya',
 'specter',
 'cattleman',
 'defected',
 'psych',
 'rightness',
 'whoppie',
 'thatcher',
 'splendid',
 'enhancement',
 'fatih',
 'cunnilingus',
 'survives',
 'cundey',
 'electric',
 'fiend',
 'foreground',
 'potent',
 'marple',
 'dongen',
 'harts',
 'writes',
 'istead',
 'empathizing',
 'present',
 'osullivan',
 'infiltration',
 'spoladore',
 'mudbank',
 'togan',
 'selve',
 'megatons',
 'reducing',
 'purveyor',
 'mist',
 'ramadi',
 'accredited',
 'loners',
 'trades',
 'fruit',
 'money',
 'seild',
 'unopposed',
 'escapee',
 'provokes',
 'hickey',
 'cornea',
 'shimada',
 'uncomfortably',
 'luege',
 'spano',
 'undefinable',
 'dutched',
 'throws',
 'mh',
 'assume',
 'affirming',
 'stripping',
 'rover',
 'coolness',
 'overemphasis',
 'retinas',
 'shyness',
 'tipple',
 'parallelisms',
 'debucourt',
 'placido',
 'wildsmith',
 'nuddie',
 'mcmaster',
 'coutts',
 'monikers',
 'fked',
 'freshly',
 'beatles',
 'whishaw',
 'string',
 'pylon',
 'greenstreet',
 'salsa',
 'jus',
 'biographys',
 'maddening',
 'bricked',
 'upturn',
 'nuyoricans',
 'aguila',
 'restructuring',
 'rupee',
 'potency',
 'effectively',
 'dupont',
 'spoiled',
 'obelisk',
 'douce',
 'dictators',
 'hasslehoff',
 'mortals',
 'marts',
 'frenchfilm',
 'possess',
 'supertexts',
 'ghatak',
 'blinders',
 'pawning',
 'saarsgard',
 'bahamas',
 'part',
 'streisand',
 'growled',
 'eighth',
 'bowersock',
 'macaroni',
 'barbarous',
 'thoughtfully',
 'glowing',
 'codfish',
 'plymouth',
 'charterers',
 'heavy',
 'exploiter',
 'prepoire',
 'mopes',
 'peer',
 'larking',
 'shae',
 'groupie',
 'littlefield',
 'gonzo',
 'policewoman',
 'thundercats',
 'pores',
 'skers',
 'dren',
 'arsewit',
 'willims',
 'violently',
 'wertmuller',
 'bitterman',
 'iordache',
 'lofaso',
 'overlook',
 'carreyed',
 'thou',
 'datta',
 'pounce',
 'shivery',
 'musical',
 'purrrrrrrrrrrrrrrr',
 'ahhhhhh',
 'daerden',
 'lestrade',
 'zeon',
 'stamped',
 'korda',
 'simpatico',
 'donnacha',
 'nothwest',
 'pit',
 'minding',
 'stribor',
 'prequel',
 'shakesphere',
 'harbour',
 'incoming',
 'leena',
 'undercurrent',
 'python',
 'paging',
 'crinkled',
 'perpetuation',
 'brackish',
 'treacherously',
 'sforza',
 'niklas',
 'marginalized',
 'fincher',
 'msr',
 'dithering',
 'stamina',
 'affected',
 'redeeming',
 'retire',
 'stewardess',
 'impersonalized',
 'quaien',
 'sipped',
 'blurt',
 'bsa',
 'gesticulating',
 'constitute',
 'ahehehe',
 'meekly',
 'gosha',
 'zimmerframe',
 'spools',
 'inexistent',
 'therefore',
 'emilia',
 'turveydrop',
 'drier',
 'ways',
 'drinking',
 'maitlan',
 'sneha',
 'bakesfield',
 'rinse',
 'slimey',
 'lozano',
 'indigo',
 'tripping',
 'subsides',
 'deludes',
 'reviews',
 'spirituality',
 'insurance',
 'scriptors',
 'precedent',
 'telescoping',
 'sirico',
 'submissions',
 'urbisci',
 'loveability',
 'aswell',
 'tested',
 'isla',
 'contribute',
 'cahill',
 'advertising',
 'cohan',
 'costello',
 'dorkiness',
 'shaw',
 'crooks',
 'bulk',
 'porn',
 'windfall',
 'mehmet',
 'engel',
 'schizo',
 'somber',
 'svengali',
 'towered',
 'witnessed',
 'worst',
 'scanned',
 'marber',
 'geez',
 'stalled',
 'averaged',
 'seaminess',
 'incidental',
 'stalk',
 'murals',
 'yas',
 'extremeley',
 'unmoored',
 'moralizing',
 'stoker',
 'matador',
 'oddest',
 'gabrielle',
 'munsters',
 'zestful',
 'tlog',
 'http',
 'houswives',
 'gras',
 'deceivingly',
 'madtv',
 'overburdening',
 'francie',
 'americanime',
 'jurassic',
 'rendevous',
 'funner',
 'colony',
 'reminisced',
 'schwarzenberg',
 'drywall',
 'findings',
 'kidnap',
 'esp',
 'quandaries',
 'advisedly',
 'fishing',
 'snubbed',
 'juliette',
 'ideologue',
 'secondaries',
 'scratcher',
 'obsessive',
 'modernization',
 'analogies',
 'braided',
 'extremity',
 'daimajin',
 'warmongering',
 'characterise',
 'conspir',
 'jokesdespite',
 'frock',
 'craziest',
 'continuing',
 'bark',
 'depending',
 'hara',
 'glazen',
 'corruption',
 'captivated',
 'teabagging',
 'renditions',
 'quitte',
 'upsidedownor',
 'cpo',
 'aggrandizement',
 'slackly',
 'beatlemaniac',
 'delli',
 'geared',
 'cleopatra',
 'jurisprudence',
 'confronts',
 'mishap',
 'caps',
 'muscats',
 'wishbone',
 'yomiuri',
 'isham',
 'quest',
 'rappers',
 'marilee',
 'jasons',
 'migenes',
 'comprises',
 'alls',
 'implausability',
 'torch',
 'settlements',
 'invisibly',
 'toyman',
 'windu',
 'dooooom',
 'whiny',
 'empathise',
 'cozied',
 'milan',
 'unzip',
 'hera',
 'marnack',
 'assult',
 'referendum',
 'louvers',
 'deherrera',
 'stubbs',
 'baloney',
 'martains',
 'manipulate',
 'frogmarched',
 'surmount',
 'elene',
 'grinders',
 'hybridnot',
 'binding',
 'significance',
 'texas',
 'zaps',
 'halfhearted',
 'donkey',
 'jamie',
 'casted',
 'mcaffe',
 'striving',
 'vegetarians',
 'jennifers',
 'decimals',
 'orchestrating',
 'gard',
 'himmler',
 'plussed',
 'cambodian',
 'blankety',
 'refreshing',
 'gloomy',
 'belgian',
 'brutal',
 'courtland',
 'screenlay',
 'outclassed',
 'ruthlessreviews',
 'schoolish',
 'baggy',
 'rwandese',
 'weasing',
 'worthlessness',
 'zarah',
 'scatchard',
 'frederik',
 'mias',
 'raksha',
 'definite',
 'cooling',
 'seraphim',
 'vanguard',
 'asano',
 'maimed',
 'wohl',
 'heuy',
 'balding',
 'earnt',
 'separately',
 'alert',
 'unearthed',
 'parvenu',
 'gunked',
 'tanaaz',
 'elitism',
 'reasonbaly',
 'pardoning',
 'saluja',
 'taximeter',
 'choreographic',
 'discipline',
 'congradulations',
 'armaments',
 'beswicke',
 'geeze',
 'cutter',
 'happenin',
 'howlers',
 'sputtered',
 'irreparably',
 'informality',
 'glancingly',
 'programme',
 'parlaying',
 'ghoul',
 'westmore',
 'philip',
 'chopping',
 'surfing',
 'raven',
 'popularize',
 'engulf',
 'carruthers',
 'czekh',
 'griswald',
 'verveen',
 'insistence',
 'weirdness',
 'wearing',
 'pills',
 'doomsville',
 'loreno',
 'maximally',
 'pestilence',
 'departing',
 'beany',
 'andr',
 'sores',
 'legged',
 'alloy',
 'rowdies',
 'sanitizes',
 'sharman',
 'honduras',
 'glares',
 'texel',
 'factness',
 'banded',
 'swathed',
 'shocky',
 'confusathon',
 'shuns',
 'commender',
 'lucinenne',
 'chapin',
 'ghosties',
 'feirstein',
 'rip',
 'illustrates',
 'earthbound',
 'behavioural',
 'speeded',
 'broome',
 'unassaulted',
 'wooster',
 'arss',
 'vindicates',
 'gabbar',
 'braintrust',
 'progressed',
 'terminatrix',
 'verbalizations',
 'dupia',
 'excactly',
 'suits',
 'babaji',
 'raschid',
 'door',
 'subset',
 'sheng',
 'traces',
 'hearken',
 'councilwoman',
 'mordant',
 'tsunami',
 'savings',
 'wrightly',
 'spite',
 'efficient',
 'beaty',
 'jingoistic',
 'ashknenazi',
 'belt',
 'peevishness',
 'pranks',
 'dingaling',
 'confederation',
 'morning',
 'hershey',
 'yablans',
 'pontificate',
 'hotdog',
 'ochoa',
 'definable',
 'bonanzas',
 'laural',
 'deus',
 'archrivals',
 'daal',
 'unintended',
 'dominique',
 'lattices',
 'ball',
 'demeaned',
 'planter',
 'francis',
 'favourites',
 'tedious',
 'matlock',
 'palmentari',
 'animaster',
 'daydreams',
 'palo',
 'bucketloads',
 'mol',
 'marshal',
 'fleece',
 'bookending',
 'offed',
 'pensacolians',
 'wean',
 'fait',
 'glaciers',
 'hoofing',
 'muffled',
 'revitalized',
 'armless',
 'tis',
 'benjy',
 'wendigo',
 'haughty',
 'edwige',
 'magnetism',
 'fortyish',
 'obese',
 'cinmatographe',
 'cavalcades',
 'ilu',
 'dissaude',
 'takkyuubin',
 'sollace',
 'persue',
 'scheduled',
 'prehysteria',
 'attracting',
 'arden',
 'facades',
 'omc',
 'lead',
 'unquestionably',
 'bostwick',
 'ornella',
 'poochie',
 'argo',
 'ninga',
 'shelf',
 'litigation',
 'ballet',
 'grooming',
 'evidente',
 'righetti',
 'reliability',
 'incapacitating',
 'allay',
 'melvyn',
 'lyons',
 'roams',
 'steinberg',
 'unsettles',
 'robotnik',
 'schindlers',
 'berkowitz',
 'uppercrust',
 'markel',
 'ecologically',
 'syvlie',
 'airports',
 'independence',
 'inauthentic',
 'sufferings',
 'fellner',
 'sternly',
 'orlans',
 'funkions',
 'regiments',
 'tranquility',
 'naista',
 'montezuma',
 'carnosaur',
 'astra',
 'manifests',
 'thieson',
 'bib',
 'simmond',
 'prosthetic',
 'uta',
 'viscerally',
 'gaolers',
 'learner',
 'cannibalize',
 'classic',
 'gilt',
 'bania',
 'filmsadly',
 'lanquage',
 'mauritania',
 'oddballs',
 'gnaw',
 'treat',
 'disregarding',
 'haley',
 'fall',
 'hickman',
 'oppress',
 'mcdonell',
 'blowtorches',
 'kneecaps',
 'initialize',
 'hottub',
 'foudre',
 'pointblank',
 'laughting',
 'merritt',
 'singlet',
 'commandments',
 'jlh',
 'abrahams',
 'recut',
 'hydraulics',
 'thomson',
 'greeeeeat',
 'unimaginably',
 'yahoo',
 'galling',
 'bowl',
 'rotted',
 'compositely',
 'ungratifying',
 'flunked',
 'mum',
 'queens',
 'avati',
 'hester',
 'tiffani',
 'treasonous',
 'alsobrook',
 'estonia',
 'wathced',
 'climatic',
 'behold',
 'printer',
 'winslet',
 'minutes',
 'glaringly',
 'quatermaine',
 'grainy',
 'nowheres',
 'lordship',
 'trespassing',
 'reorder',
 'fizzles',
 'banana',
 'diner',
 'carotte',
 'nina',
 'jabbering',
 'michale',
 'dispersement',
 'insignificance',
 'obscenely',
 'riccardo',
 'killed',
 'truffle',
 'vivian',
 'aristotle',
 'emilie',
 'vacancy',
 'changruputra',
 'conrad',
 'edelman',
 'kar',
 'trotta',
 'thickener',
 'rajpal',
 'windstorm',
 'sylke',
 'fascinating',
 'haj',
 'iconor',
 'bensen',
 'trelkovski',
 'filmcow',
 'mente',
 'grades',
 'pontiac',
 'carltio',
 'hillside',
 'greatfully',
 'makepease',
 'saaad',
 'pungency',
 'decides',
 'migraine',
 'condensing',
 'crowned',
 'cop',
 'mythos',
 'crandall',
 'merendino',
 'unrecognizable',
 'apparatchik',
 'swimmers',
 'cadences',
 'campfield',
 'eastward',
 'smight',
 'valdez',
 'guises',
 'cameroon',
 'segregating',
 'hardin',
 'krupa',
 'woodfin',
 'albans',
 'kitchener',
 'pharmaceutical',
 'lithe',
 'responded',
 'ebullient',
 'fonzie',
 'cramer',
 'morena',
 'contemp',
 'pods',
 'sherwood',
 'schuer',
 'dck',
 'reinstall',
 'spenny',
 'dodeskaden',
 'daz',
 'tantalizing',
 'microsoft',
 'symbolically',
 'nationalities',
 'felisberto',
 'stan',
 'shubert',
 ...]

In [23]:

    

import numpy as np

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


    

    
Out[23]:





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

In [47]:

    

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


    

    
Out[47]:

In [25]:

    

word2index = {}

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


    

    
Out[25]:





{'': 0,
 'cockfighting': 1,
 'companion': 2,
 'metaphorically': 3,
 'bureaucrats': 4,
 'lollies': 5,
 'cannabis': 6,
 'portraited': 7,
 'zones': 8,
 'mcliam': 9,
 'goldustluna': 10,
 'voices': 11,
 'vigilant': 12,
 'supes': 13,
 'madeasuck': 14,
 'hbo': 15,
 'trys': 16,
 'soundly': 17,
 'airways': 18,
 'fintail': 19,
 'munched': 20,
 'pointe': 21,
 'meal': 22,
 'carver': 23,
 'hoops': 24,
 'dazzlingly': 25,
 'capitalist': 26,
 'testicle': 27,
 'karogi': 28,
 'stifle': 29,
 'temples': 30,
 'suspicious': 31,
 'reigert': 32,
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 'merendino': 960,
 'unrecognizable': 961,
 'apparatchik': 962,
 'swimmers': 963,
 'cadences': 964,
 'campfield': 965,
 'eastward': 966,
 'smight': 967,
 'valdez': 968,
 'guises': 969,
 'cameroon': 970,
 'segregating': 971,
 'hardin': 972,
 'krupa': 973,
 'woodfin': 974,
 'albans': 975,
 'kitchener': 976,
 'pharmaceutical': 977,
 'lithe': 978,
 'responded': 979,
 'ebullient': 980,
 'fonzie': 981,
 'cramer': 982,
 'morena': 983,
 'contemp': 984,
 'pods': 985,
 'sherwood': 986,
 'schuer': 987,
 'dck': 988,
 'reinstall': 989,
 'spenny': 990,
 'dodeskaden': 991,
 'daz': 992,
 'tantalizing': 993,
 'microsoft': 994,
 'symbolically': 995,
 'nationalities': 996,
 'felisberto': 997,
 'stan': 998,
 'shubert': 999,
 ...}

In [26]:

    

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 [27]:

    

layer_0


    

    
Out[27]:





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

In [60]:

    

y = [1 if l=="POSITIVE" else 0 for l in labels]
y[:10]


    

    
Out[60]:





[1, 0, 1, 0, 1, 0, 1, 0, 1, 0]

In [28]:

    

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


    

In [29]:

    

labels[0]


    

    
Out[29]:





'POSITIVE'

In [30]:

    

get_target_for_label(labels[0])


    

    
Out[30]:





1

In [31]:

    

labels[1]


    

    
Out[31]:





'NEGATIVE'

In [32]:

    

get_target_for_label(labels[1])


    

    
Out[32]:





0

In [49]:

    

a = [[4,5]]
print(a)
a.append([6])
a


    

    




[[4, 5]]






    
Out[49]:





[[4, 5], [6]]

In [51]:

    

x = []
for review in reviews:
    update_input_layer(review)
    x.append(layer_0)
x[:5]


    

    
Out[51]:





[array([[ 24.,   0.,   0., ...,   0.,   0.,   0.]]),
 array([[ 24.,   0.,   0., ...,   0.,   0.,   0.]]),
 array([[ 24.,   0.,   0., ...,   0.,   0.,   0.]]),
 array([[ 24.,   0.,   0., ...,   0.,   0.,   0.]]),
 array([[ 24.,   0.,   0., ...,   0.,   0.,   0.]])]

In [53]:

    

x = np.array(x)
x[:5]


    

    
Out[53]:





array([[[ 24.,   0.,   0., ...,   0.,   0.,   0.]],

       [[ 24.,   0.,   0., ...,   0.,   0.,   0.]],

       [[ 24.,   0.,   0., ...,   0.,   0.,   0.]],

       [[ 24.,   0.,   0., ...,   0.,   0.,   0.]],

       [[ 24.,   0.,   0., ...,   0.,   0.,   0.]]])

In [55]:

    

y = [get_target_for_label(l) for l in labels] 
for i in range(10):
    print(labels[i], y[i])
y[:20]


    

    




POSITIVE 1
NEGATIVE 0
POSITIVE 1
NEGATIVE 0
POSITIVE 1
NEGATIVE 0
POSITIVE 1
NEGATIVE 0
POSITIVE 1
NEGATIVE 0






    
Out[55]:





[1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0]

In [ ]:

    

import time
import sys
import numpy as np

class SentimentNetwork(object):
    def __init__(self, reviews, labels, hidden_nodes=10, output_nodes=1, 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)
        
        print('NeuralNet intiated!')
        print('Nodes:', self.input_nodes, self.hidden_nodes, self.output_nodes)
        print('learning rate: ', self.learning_rate)

        
    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 sigmoid(self, x):
        return 1 / (1 + np.exp(-x))
        
    def sigmoid_output_2_derivative(self,output):
        return output * (1 - output)
    
    def pre_process_data(self, reviews, labels):
        assert len(reviews) == len(labels)
        
        #self.y = [1 if label=="POSITIVE" else 0 for label in 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 get_target_for_label(self, label):
        if(label == 'POSITIVE'):
            return 1
        else:
            return 0
    
    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 train(self, inputs_list, targets_list):
        assert len(inputs_list) == len(targets_list)
        correct_so_far = 0
        start = time.time()
        
        for i in range(len(inputs_list)):
            review = inputs_list[i]
            label = targets_list[i]
            label_value = self.get_target_for_label(label)
            
            # Convert inputs list to 2d array
            #inputs = np.array(inputs_list, ndmin=2).T
            #targets = np.array(targets_list, ndmin=2).T

            #### Implement the forward pass here ####
            ### Forward pass ###
            
            self.update_input_layer(review)
            
            # input layer
            layer_1 = np.dot(self.layer_0, self.weights_0_1)
            
            # output layer
            layer_2 = self.sigmoid(np.dot(layer_1, self.weights_1_2))
            
            
            #### Implement the backward pass here ####
            ### Backward pass ###

            # TODO: Output error
            layer_2_errors = layer_2 # Output layer error is the difference between desired target and actual output.
            layer_2_delta = layer_2_errors * self.sigmoid_output_2_derivative(layer_2)

            # TODO: Backpropagated error
            layer_1_errors = np.dot(layer_2_delta, self.weights_1_2.T) # errors propagated to the hidden layer
            layer_1_delta = layer_1_errors # hidden layer gradients
            
            # 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_errors) < 0.5):
                correct_so_far += 1
            
            reviews_per_second = i / float(time.time() - start)
            
            sys.stdout.write("\rProgress:" + str(100 * i/float(len(inputs_list)))[: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"
    
def MSE(y, Y):
    return np.mean((y-Y)**2)


    

In [141]:

    

### Set the hyperparameters here ###
epochs = 100
learning_rate = 0.04
hidden_nodes = 2
output_nodes = 1

mlp = SentimentNetwork(reviews[:-1000],labels[:-1000], learning_rate=1.0)

print(mlp.weights_0_1)
print(mlp.weights_1_2)

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


    

    




NeuralNet intiated!
Nodes: 72810 10 1
learning rate:  1.0
[[ 0.  0.  0. ...,  0.  0.  0.]
 [ 0.  0.  0. ...,  0.  0.  0.]
 [ 0.  0.  0. ...,  0.  0.  0.]
 ..., 
 [ 0.  0.  0. ...,  0.  0.  0.]
 [ 0.  0.  0. ...,  0.  0.  0.]
 [ 0.  0.  0. ...,  0.  0.  0.]]
[[ 1.62434536]
 [-0.61175641]
 [-0.52817175]
 [-1.07296862]
 [ 0.86540763]
 [-2.3015387 ]
 [ 1.74481176]
 [-0.7612069 ]
 [ 0.3190391 ]
 [-0.24937038]]
Progress:99.9% Speed(reviews/sec):446.7% #Correct:500 #Tested:1000 Testing Accuracy:50.0%

In [142]:

    

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


    

    




1.0
Progress:0.0% Speed(reviews/sec):0.0 #Correct:0 #Trained:1 Training Accuracy:0.0%
Progress:0.05% Speed(reviews/sec):70.68 #Correct:14 #Trained:15 Training Accuracy:93.3%





    




/Users/ko/anaconda/lib/python3.6/site-packages/ipykernel/__main__.py:36: RuntimeWarning: overflow encountered in exp






    




Progress:10.4% Speed(reviews/sec):94.26 #Correct:2500 #Trained:2501 Training Accuracy:99.9%
Progress:20.8% Speed(reviews/sec):97.80 #Correct:5000 #Trained:5001 Training Accuracy:99.9%
Progress:30.4% Speed(reviews/sec):98.13 #Correct:7312 #Trained:7313 Training Accuracy:99.9%





    




---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
<ipython-input-142-71f3c6a6a52d> in <module>()
      1 # train the network
      2 print(mlp.learning_rate)
----> 3 mlp.train(reviews[:-1000],labels[:-1000])

<ipython-input-129-e0a9221a980e> in train(self, inputs_list, targets_list)
    122             # TODO: Update the weights
    123             self.weights_1_2 -= layer_1.T.dot(layer_2_delta) * self.learning_rate # update hidden-to-output weights with gradient descent step
--> 124             self.weights_0_1 -= self.layer_0.T.dot(layer_1_delta) * self.learning_rate # update input-to-hidden weights with gradient descent step
    125 
    126             if(np.abs(layer_2_errors) < 0.5):

KeyboardInterrupt: 

In [ ]:

    

print(mlp.weights_0_1)
print(mlp.weights_1_2)
mlp.test(reviews[-1000:],labels[-1000:])


    

In [ ]: