In [2]:

    

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 [3]:

    

len(reviews)


    

    
Out[3]:





25000

In [4]:

    

reviews[0]


    

    
Out[4]:





'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 [5]:

    

labels[0]


    

    
Out[5]:





'POSITIVE'

In [6]:

    

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 [7]:

    

from collections import Counter
import numpy as np


    

In [8]:

    

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


    

In [9]:

    

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 [10]:

    

positive_counts.most_common()


    

    
Out[10]:





[('', 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 [11]:

    

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 [12]:

    

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


    

    
Out[12]:





[('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),
 ('poignant', 1.4022947024663317),
 ('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),
 ('aunt', 1.023388867430522),
 ('walter', 1.022328983918479),
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 ('portrayal', 1.0189810189761024),
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 ('awe', 0.97186058302896583),
 ('vhs', 0.97116734209998934),
 ('eerie', 0.97116734209998934),
 ('lonely', 0.96873720724669754),
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 ('danny', 0.93967118051821874),
 ('anime', 0.93378388932167222),
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 ('mysteries', 0.92953595862417571),
 ('hal', 0.92953595862417571),
 ('louis', 0.92871325187271225),
 ('charming', 0.92520609553210742),
 ('urban', 0.92367083917177761),
 ('allows', 0.92183091224977043),
 ('impact', 0.91815814604895041),
 ('italy', 0.91629073187415511),
 ('gradually', 0.91629073187415511),
 ('lifestyle', 0.91629073187415511),
 ('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 [13]:

    

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


    

    
Out[13]:





[('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 [14]:

    

from IPython.display import Image

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

Image(filename='sentiment_network.png')


    

    
Out[14]:

In [15]:

    

review = "The movie was excellent"

Image(filename='sentiment_network_pos.png')


    

    
Out[15]:

In [16]:

    

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


    

    




74074

In [17]:

    

list(vocab)


    

    
Out[17]:





['',
 'kindhearted',
 'armageddon',
 'alleyways',
 'implicating',
 'lush',
 'painterly',
 'porshe',
 'lugacy',
 'ondine',
 'carrot',
 'swathe',
 'unredeemed',
 'spijun',
 'francais',
 'planetscapes',
 'gravestone',
 'bridge',
 'sharif',
 'nothan',
 'astronomical',
 'sendup',
 'condense',
 'chesapeake',
 'dyslexic',
 'languor',
 'poupon',
 'livvakterna',
 'pressence',
 'ubba',
 'freshmen',
 'twizzlers',
 'duquesne',
 'rehearsing',
 'prostitutes',
 'handicaps',
 'claustrophobic',
 'cinematicism',
 'approxamitly',
 'cantics',
 'takiya',
 'idris',
 'masturbated',
 'superfluous',
 'zanuck',
 'lydia',
 'locus',
 'scooping',
 'leith',
 'towelheads',
 'embattled',
 'confound',
 'reiju',
 'texans',
 'misdirecting',
 'professionally',
 'banana',
 'gregor',
 'owning',
 'grecianized',
 'swatman',
 'unrestrainedly',
 'petunia',
 'oooo',
 'philandering',
 'schmidt',
 'stemmed',
 'adversity',
 'artisty',
 'contrast',
 'tournament',
 'detmer',
 'responses',
 'behaves',
 'madnes',
 'aince',
 'female',
 'swanks',
 'sheedy',
 'grateful',
 'terrorvision',
 'senselessness',
 'evertytime',
 'platte',
 'plesantly',
 'alucard',
 'intersects',
 'pickens',
 'utans',
 'bleached',
 'goodies',
 'mickie',
 'assaults',
 'admittance',
 'chamcha',
 'refinery',
 'represses',
 'walkleys',
 'sinker',
 'heth',
 'award',
 'recapitulate',
 'oppression',
 'palimpsest',
 'crains',
 'amicably',
 'farnel',
 'ahamad',
 'impetus',
 'whoo',
 'shimmering',
 'bierko',
 'unreadable',
 'workmates',
 'nomad',
 'troublesome',
 'debate',
 'fireballs',
 'unconstructive',
 'dickson',
 'arduously',
 'accusatory',
 'confiscated',
 'influenced',
 'azn',
 'languorously',
 'clamour',
 'crucial',
 'noteworthy',
 'entrancingly',
 'flubber',
 'deadringer',
 'erase',
 'gearing',
 'munchie',
 'inflating',
 'polarizing',
 'sedately',
 'yer',
 'subbed',
 'vomitory',
 'communion',
 'preside',
 'shahan',
 'hightower',
 'eurosleaze',
 'gemma',
 'embryonic',
 'sudser',
 'rosenliski',
 'figment',
 'outers',
 'sydney',
 'decrying',
 'ql',
 'meanderings',
 'principal',
 'knuckle',
 'enmeshed',
 'cadena',
 'jammer',
 'discredit',
 'deadliest',
 'chew',
 'boyars',
 'rereads',
 'staffer',
 'eads',
 'souly',
 'nursing',
 'sweetly',
 'wagging',
 'slade',
 'bette',
 'ozone',
 'architecture',
 'sexism',
 'protocol',
 'pricks',
 'belongings',
 'enlisted',
 'transients',
 'marish',
 'ibiza',
 'grabs',
 'embarrassing',
 'arnolds',
 'frenegonde',
 'reporter',
 'popularism',
 'muddy',
 'winckler',
 'downingtown',
 'checkered',
 'condieff',
 'bratty',
 'overpraise',
 'smarmy',
 'upchucking',
 'ethically',
 'captivatingly',
 'nets',
 'donners',
 'gi',
 'kayaks',
 'truckloads',
 'untainted',
 'survival',
 'whereever',
 'chagrin',
 'dada',
 'curtain',
 'lldoit',
 'emasculate',
 'sufferance',
 'ayin',
 'wouldnt',
 'lorenz',
 'civilizations',
 'winglies',
 'lulls',
 'festivals',
 'stirringly',
 'jeyaraj',
 'blarney',
 'speculates',
 'ingrid',
 'cxxp',
 'felicities',
 'acidity',
 'unclad',
 'hemmerling',
 'overreact',
 'carpet',
 'rumbling',
 'sherbert',
 'abruptness',
 'upendra',
 'axe',
 'yorkers',
 'eyedots',
 'weisz',
 'agenda',
 'arturo',
 'unnattractive',
 'lagrimas',
 'tlps',
 'ineffectual',
 'creeps',
 'ethiopia',
 'itch',
 'batgirl',
 'choreography',
 'quatermass',
 'blazed',
 'leung',
 'assimilate',
 'spines',
 'eratic',
 'anbody',
 'oooh',
 'cancan',
 'negotiatior',
 'recipients',
 'electrolytes',
 'hordern',
 'overpower',
 'fearlessly',
 'seymore',
 'unbeatable',
 'iordache',
 'downed',
 'beating',
 'harlin',
 'ai',
 'nicola',
 'aaaaah',
 'nabiki',
 'proclaims',
 'mattresses',
 'considerable',
 'sodomizing',
 'mcnear',
 'epiphany',
 'moltisanti',
 'replaced',
 'jackpot',
 'dukas',
 'benny',
 'archive',
 'deluge',
 'picnics',
 'agnisakshi',
 'thuglife',
 'pfiefer',
 'digs',
 'panicked',
 'nba',
 'omens',
 'sherlyn',
 'cobblestoned',
 'dazzling',
 'unabashed',
 'woken',
 'yoshinoya',
 'likelew',
 'soiled',
 'hening',
 'kev',
 'schuer',
 'frightening',
 'ongoings',
 'unerringly',
 'brokered',
 'rhythmically',
 'hackett',
 'cheery',
 'misrepresentative',
 'emptiness',
 'kotex',
 'pompom',
 'row',
 'peat',
 'reciprocate',
 'chilling',
 'prudence',
 'gunna',
 'bureaucrat',
 'rigorous',
 'joyously',
 'receiving',
 'refraining',
 'werent',
 'gonzo',
 'plz',
 'maclean',
 'resistance',
 'prophecies',
 'rotld',
 'gildersleeves',
 'balloonist',
 'auction',
 'maupins',
 'eacb',
 'adjustments',
 'cigliutti',
 'culbertson',
 'stalemate',
 'aplogise',
 'locks',
 'fredos',
 'sixstar',
 'vt',
 'tooie',
 'fouls',
 'camelot',
 'oratorio',
 'mikimoto',
 'robald',
 'flowes',
 'stormy',
 'grumble',
 'commercialized',
 'marauds',
 'fantabulous',
 'lucked',
 'anticlimatic',
 'michal',
 'luque',
 'exaggerates',
 'camp',
 'hoosegow',
 'coarseness',
 'ceramic',
 'manically',
 'bryant',
 'ancien',
 'backstabber',
 'primitiveness',
 'trespass',
 'cultivated',
 'muddle',
 'frogmarched',
 'manly',
 'zima',
 'manning',
 'worried',
 'sequence',
 'companionship',
 'reasonit',
 'homoeroticisms',
 'higgins',
 'tweeners',
 'balderdash',
 'irrational',
 'shakingly',
 'auroras',
 'hammerheadshark',
 'salik',
 'teensy',
 'gradef',
 'fidenco',
 'zoloft',
 'jik',
 'disowning',
 'metacinema',
 'nerd',
 'freudstein',
 'violently',
 'humanoids',
 'hyderabadi',
 'cobbler',
 'inhibition',
 'redundancies',
 'gaped',
 'crawford',
 'neve',
 'embroidered',
 'luci',
 'plummeted',
 'hungers',
 'inground',
 'braselle',
 'whiile',
 'girlishness',
 'caressing',
 'sayid',
 'antagonizes',
 'mannequin',
 'grampa',
 'myazaki',
 'krisak',
 'naranjos',
 'extrapolation',
 'clearest',
 'hollander',
 'wqasn',
 'tooltime',
 'gown',
 'bloodsucking',
 'curling',
 'crisply',
 'polymath',
 'pathological',
 'gunbuster',
 'perversity',
 'shud',
 'professione',
 'solos',
 'polyester',
 'paring',
 'hitcock',
 'guaging',
 'voorhees',
 'momentarily',
 'disguises',
 'gethsemane',
 'hartnell',
 'economic',
 'gagnon',
 'yeah',
 'mozambique',
 'yahoo',
 'records',
 'estefan',
 'isaac',
 'daringly',
 'samways',
 'cavalier',
 'galaxy',
 'showerman',
 'frocked',
 'nique',
 'barbirino',
 'threequels',
 'electecuted',
 'mustangs',
 'baser',
 'uncalled',
 'entomologist',
 'zubeidaa',
 'vet',
 'yakitate',
 'procedure',
 'els',
 'deformed',
 'nielsen',
 'carbide',
 'nazi',
 'deterioration',
 'mccartle',
 'helming',
 'harmonious',
 'attractiveness',
 'menen',
 'tiresome',
 'ridiculously',
 'protaganiste',
 'liddle',
 'conquistador',
 'impress',
 'pard',
 'agnus',
 'francoise',
 'sexy',
 'emphatically',
 'bonham',
 'thoe',
 'bruno',
 'slate',
 'rpg',
 'saltshaker',
 'cranberry',
 'lifeboats',
 'raging',
 'marianne',
 'tryfon',
 'push',
 'spore',
 'mandell',
 'villasenor',
 'widdered',
 'superheros',
 'nefarious',
 'assassins',
 'getz',
 'despairable',
 'versace',
 'dandyish',
 'frisbee',
 'fistsof',
 'hosted',
 'carriages',
 'assumptions',
 'thrift',
 'formulate',
 'wendt',
 'wrapped',
 'threw',
 'asbestos',
 'boffin',
 'zifferedi',
 'disassociative',
 'meowed',
 'nightmaressuch',
 'fisherman',
 'hoskins',
 'hahaha',
 'blackbuster',
 'evaluated',
 'pffffft',
 'escreve',
 'hereby',
 'villain',
 'schepsi',
 'mightn',
 'konrack',
 'lattes',
 'psychoanalyzing',
 'mesmerize',
 'screenwriter',
 'laufther',
 'tolmekians',
 'farrells',
 'crackers',
 'vaughn',
 'relocated',
 'heeru',
 'robots',
 'sinden',
 'tugboat',
 'augments',
 'smallest',
 'breathtakingly',
 'matt',
 'enshrouded',
 'patriotism',
 'reconcile',
 'nobodys',
 'have',
 'compression',
 'porridge',
 'jussi',
 'calmness',
 'guidos',
 'maul',
 'irony',
 'dandelion',
 'kerry',
 'culp',
 'ishwar',
 'babaji',
 'isint',
 'oakland',
 'bar',
 'posturing',
 'crapola',
 'okanagan',
 'volney',
 'undying',
 'hiarity',
 'somnolent',
 'pony',
 'deserved',
 'xtianity',
 'nipponjin',
 'memoir',
 'zavattini',
 'soren',
 'protesters',
 'bastidge',
 'tilse',
 'gia',
 'porker',
 'wonman',
 'condoms',
 'beret',
 'trucks',
 'shoots',
 'smoker',
 'swimmer',
 'assessed',
 'escadrille',
 'organisms',
 'vi',
 'dresser',
 'affleck',
 'endulge',
 'semaphore',
 'excellency',
 'regaining',
 'confuddled',
 'somebody',
 'flacks',
 'vdb',
 'bhangra',
 'ragpal',
 'striker',
 'diabolism',
 'ashwood',
 'ingenuity',
 'turnstiles',
 'oral',
 'orlac',
 'bhi',
 'theirry',
 'jaded',
 'wallet',
 'hounds',
 'veeeeeeeery',
 'nihilistically',
 'levres',
 'grope',
 'asl',
 'collapsing',
 'pittsburgh',
 'nostradamus',
 'valkyrie',
 'tamlyn',
 'fretting',
 'yeow',
 'resented',
 'aod',
 'sousa',
 'finger',
 'alastair',
 'blackhawk',
 'poignancy',
 'litel',
 'disciplines',
 'chopped',
 'nowadays',
 'negligible',
 'tout',
 'databanks',
 'sporatically',
 'shotty',
 'crawler',
 'clubfoot',
 'elfriede',
 'gravy',
 'solaris',
 'hysterical',
 'priyadarshan',
 'cajoling',
 'wooing',
 'parameters',
 'rye',
 'purblind',
 'mccurdy',
 'corenblith',
 'drone',
 'turgenev',
 'ratt',
 'maiko',
 'velde',
 'parents',
 'steppers',
 'brusk',
 'culpas',
 'laroche',
 'hopton',
 'alchemize',
 'cdg',
 'overpopulated',
 'gunfights',
 'beattie',
 'human',
 'fata',
 'wastelands',
 'customized',
 'worlds',
 'hatosy',
 'potente',
 'twitches',
 'leibman',
 'carrer',
 'gabriel',
 'revived',
 'jennifer',
 'falon',
 'ravera',
 'transformational',
 'shaves',
 'arlook',
 'federation',
 'flamingo',
 'harrold',
 'puritan',
 'superceeds',
 'surest',
 'goodall',
 'bopping',
 'litvak',
 'viewed',
 'moisturiser',
 'aahhh',
 'gifting',
 'bsu',
 'constipated',
 'decoy',
 'elpidia',
 'reactive',
 'peed',
 'fax',
 'janne',
 'appr',
 'beset',
 'marksman',
 'robertsons',
 'parasite',
 'irretrievable',
 'symphonie',
 'nco',
 'batter',
 'miklos',
 'search',
 'apologies',
 'liba',
 'pumphrey',
 'comprises',
 'obee',
 'nobleman',
 'sudern',
 'blessed',
 'diabolists',
 'clothing',
 'unfulfilled',
 'cosy',
 'mitchell',
 'recent',
 'videogames',
 'caridad',
 'eeeeeeeek',
 'deosnt',
 'hocked',
 'schlettow',
 'unjaded',
 'physcedelic',
 'coranado',
 'bushel',
 'travelodge',
 'tiananmen',
 'reaches',
 'conduit',
 'esperanto',
 'rubbishes',
 'liege',
 'fully',
 'astor',
 'therethat',
 'piled',
 'location',
 'barn',
 'sundayafternoon',
 'side',
 'gables',
 'oi',
 'alanis',
 'stared',
 'kascier',
 'lynches',
 'railly',
 'riggers',
 'datta',
 'saleswoman',
 'excess',
 'excelsior',
 'edwedge',
 'untutored',
 'ebony',
 'tangere',
 'anatole',
 'rosenmller',
 'weakening',
 'diggs',
 'hershman',
 'golden',
 'odete',
 'distinct',
 'ustinov',
 'humor',
 'misfires',
 'snooping',
 'ophuls',
 'rollo',
 'aimanov',
 'authenticity',
 'beekeepers',
 'struggle',
 'adorn',
 'inconceivably',
 'wwwf',
 'brauss',
 'squares',
 'azkaban',
 'gruanted',
 'bohemian',
 'unvalidated',
 'fanbases',
 'backwater',
 'mourned',
 'merely',
 'toxic',
 'rainers',
 'libertine',
 'felled',
 'afew',
 'westerberg',
 'ioc',
 'stratton',
 'yiannis',
 'cundy',
 'strum',
 'parties',
 'grumbled',
 'nuremberg',
 'breached',
 'fallouts',
 'adreno',
 'andreu',
 'unplug',
 'scuttle',
 'cant',
 'rigg',
 'sailormoon',
 'solidity',
 'samaha',
 'temperament',
 'jaffrey',
 'crypton',
 'barbet',
 'hendrick',
 'imbues',
 'hunched',
 'zionism',
 'skinned',
 'defalting',
 'stimulate',
 'throttle',
 'incorporated',
 'notting',
 'brielfy',
 'doctorate',
 'declines',
 'exceed',
 'imperfect',
 'sinus',
 'lupton',
 'crotons',
 'rejoined',
 'sudden',
 'tri',
 'balloon',
 'sculpt',
 'humpp',
 'leaping',
 'photo',
 'plunging',
 'salka',
 'beaded',
 'microbiology',
 'loused',
 'cheapie',
 'voyeurs',
 'kollek',
 'hippler',
 'funner',
 'endemic',
 'greyhound',
 'lifestyle',
 'andreas',
 'maximilian',
 'publicized',
 'catholic',
 'reflex',
 'boyz',
 'nightfall',
 'internationales',
 'ziv',
 'kassir',
 'mildest',
 'sturges',
 'saviour',
 'anointing',
 'locken',
 'latke',
 'acadmey',
 'tunnels',
 'undercut',
 'smith',
 'leathal',
 'inaugurate',
 'chess',
 'maligned',
 'eventuates',
 'haggerty',
 'deterent',
 'unstartled',
 'deceit',
 'uppermost',
 'comfortable',
 'oneliners',
 'intrigue',
 'cassevetes',
 'tummy',
 'wavers',
 'payroll',
 'hellhole',
 'reconaissance',
 'soh',
 'strait',
 'vies',
 'klause',
 'lorna',
 'injuries',
 'stale',
 'uneducated',
 'crochety',
 'flowed',
 'shtick',
 'hulkamaniacs',
 'bostid',
 'julliard',
 'dagon',
 'incarceration',
 'sexaholic',
 'pufnstuf',
 'readymade',
 'kidnapped',
 'merlet',
 'maraglia',
 'plasterboard',
 'text',
 'sequituurs',
 'embarrasment',
 'maternity',
 'precluded',
 'craptitude',
 'ba',
 'outrages',
 'bytch',
 'backbone',
 'assisted',
 'rejects',
 'vigorously',
 'catering',
 'choti',
 'titular',
 'heroines',
 'homeland',
 'trapeze',
 'plato',
 'supermodels',
 'leitmotif',
 'reiner',
 'tents',
 'echoing',
 'elmore',
 'smelly',
 ...]

In [18]:

    

import numpy as np

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


    

    
Out[18]:





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

In [19]:

    

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


    

    
Out[19]:

In [20]:

    

word2index = {}

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


    

    
Out[20]:





{'': 0,
 'kindhearted': 1,
 'armageddon': 2,
 'alleyways': 3,
 'implicating': 4,
 'lush': 5,
 'painterly': 6,
 'porshe': 7,
 'lugacy': 8,
 'ondine': 9,
 'carrot': 10,
 'swathe': 11,
 'unredeemed': 12,
 'spijun': 13,
 'francais': 14,
 'planetscapes': 15,
 'gravestone': 16,
 'bridge': 17,
 'sharif': 18,
 'nothan': 19,
 'astronomical': 20,
 'sendup': 21,
 'condense': 22,
 'chesapeake': 23,
 'dyslexic': 24,
 'languor': 25,
 'poupon': 26,
 'livvakterna': 27,
 'pressence': 28,
 'ubba': 29,
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 'uneducated': 957,
 'crochety': 958,
 'flowed': 959,
 'shtick': 960,
 'hulkamaniacs': 961,
 'bostid': 962,
 'julliard': 963,
 'dagon': 964,
 'incarceration': 965,
 'sexaholic': 966,
 'pufnstuf': 967,
 'readymade': 968,
 'kidnapped': 969,
 'merlet': 970,
 'maraglia': 971,
 'plasterboard': 972,
 'text': 973,
 'sequituurs': 974,
 'embarrasment': 975,
 'maternity': 976,
 'precluded': 977,
 'craptitude': 978,
 'ba': 979,
 'outrages': 980,
 'bytch': 981,
 'backbone': 982,
 'assisted': 983,
 'rejects': 984,
 'vigorously': 985,
 'catering': 986,
 'choti': 987,
 'titular': 988,
 'heroines': 989,
 'homeland': 990,
 'trapeze': 991,
 'plato': 992,
 'supermodels': 993,
 'leitmotif': 994,
 'reiner': 995,
 'tents': 996,
 'echoing': 997,
 'elmore': 998,
 'smelly': 999,
 ...}

In [21]:

    

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 [22]:

    

layer_0


    

    
Out[22]:





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

In [23]:

    

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


    

In [24]:

    

labels[0]


    

    
Out[24]:





'POSITIVE'

In [25]:

    

get_target_for_label(labels[0])


    

    
Out[25]:





1

In [26]:

    

labels[1]


    

    
Out[26]:





'NEGATIVE'

In [27]:

    

get_target_for_label(labels[1])


    

    
Out[27]:





0

In [33]:

    

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 [29]:

    

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


    

In [61]:

    

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


    

    




Progress:99.9% Speed(reviews/sec):587.5% #Correct:500 #Tested:1000 Testing Accuracy:50.0%

In [62]:

    

# 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):89.58 #Correct:1250 #Trained:2501 Training Accuracy:49.9%
Progress:20.8% Speed(reviews/sec):95.03 #Correct:2500 #Trained:5001 Training Accuracy:49.9%
Progress:27.4% Speed(reviews/sec):95.46 #Correct:3295 #Trained:6592 Training Accuracy:49.9%





    




---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
<ipython-input-62-d0f5d85ad402> in <module>()
      1 # train the network
----> 2 mlp.train(reviews[:-1000],labels[:-1000])

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

KeyboardInterrupt: 

In [63]:

    

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


    

In [64]:

    

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


    

    




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





    




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

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

KeyboardInterrupt: 

In [65]:

    

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


    

In [66]:

    

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


    

    




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





    




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

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

KeyboardInterrupt: 

In [67]:

    

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


    

    
Out[67]:

In [30]:

    

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 [31]:

    

layer_0


    

    
Out[31]:





array([[ 1.,  0.,  0., ...,  0.,  0.,  0.]])

In [79]:

    

review_counter = Counter()


    

In [80]:

    

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


    

In [81]:

    

review_counter.most_common()


    

    
Out[81]:





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

In [34]:

    

mlp = SentimentNetwork(reviews[:-1000],labels[:-1000], learning_rate=0.001)
# 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):98.08 #Correct:1940 #Trained:2501 Training Accuracy:77.5%
Progress:20.8% Speed(reviews/sec):98.08 #Correct:3987 #Trained:5001 Training Accuracy:79.7%
Progress:31.2% Speed(reviews/sec):97.92 #Correct:6085 #Trained:7501 Training Accuracy:81.1%
Progress:41.6% Speed(reviews/sec):98.02 #Correct:8204 #Trained:10001 Training Accuracy:82.0%
Progress:52.0% Speed(reviews/sec):95.61 #Correct:10337 #Trained:12501 Training Accuracy:82.6%
Progress:62.5% Speed(reviews/sec):94.45 #Correct:12423 #Trained:15001 Training Accuracy:82.8%
Progress:72.9% Speed(reviews/sec):94.68 #Correct:14524 #Trained:17501 Training Accuracy:82.9%
Progress:83.3% Speed(reviews/sec):94.73 #Correct:16697 #Trained:20001 Training Accuracy:83.4%
Progress:93.7% Speed(reviews/sec):94.52 #Correct:18856 #Trained:22501 Training Accuracy:83.8%
Progress:99.9% Speed(reviews/sec):94.34 #Correct:20172 #Trained:24000 Training Accuracy:84.0%

In [50]:

    

mlp.run("very nice")


    

    
Out[50]:





'POSITIVE'

In [52]:

    

mlp.run("bad film")


    

    
Out[52]:





'NEGATIVE'

In [53]:

    

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


    

    




Progress:99.9% Speed(reviews/sec):680.5% #Correct:848 #Tested:1000 Testing Accuracy:84.8%