Sentiment Classification & How To "Frame Problems" for a Neural Network

by Andrew Trask

What You Should Already Know

  • neural networks, forward and back-propagation
  • stochastic gradient descent
  • mean squared error
  • and train/test splits

Where to Get Help if You Need it

  • Re-watch previous Udacity Lectures
  • Leverage the recommended Course Reading Material - Grokking Deep Learning (40% Off: traskud17)
  • Shoot me a tweet @iamtrask

Tutorial Outline:

  • Intro: The Importance of "Framing a Problem"
  • Curate a Dataset
  • Developing a "Predictive Theory"
  • PROJECT 1: Quick Theory Validation
  • Transforming Text to Numbers
  • PROJECT 2: Creating the Input/Output Data
  • Putting it all together in a Neural Network
  • PROJECT 3: Building our Neural Network
  • Understanding Neural Noise
  • PROJECT 4: Making Learning Faster by Reducing Noise
  • Analyzing Inefficiencies in our Network
  • PROJECT 5: Making our Network Train and Run Faster
  • Further Noise Reduction
  • PROJECT 6: Reducing Noise by Strategically Reducing the Vocabulary
  • Analysis: What's going on in the weights?

Lesson: Curate a Dataset


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'

Lesson: Develop a Predictive Theory


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...

Project 1: Quick Theory Validation


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),
 ('spectacular', 1.0198314108149955),
 ('portrayal', 1.0189810189761024),
 ('ann', 1.0127808528183286),
 ('enterprise', 1.0116009116784799),
 ('musicals', 1.0096648026516135),
 ('deeply', 1.0094845087721023),
 ('incredible', 1.0061677561461084),
 ('mature', 1.0060195018402847),
 ('triumph', 0.99682959435816731),
 ('margaret', 0.99682959435816731),
 ('navy', 0.99493385919326827),
 ('harry', 0.99176919305006062),
 ('lucas', 0.990398704027877),
 ('sweet', 0.98966110487955483),
 ('joey', 0.98794672078059009),
 ('oscar', 0.98721905111049713),
 ('balance', 0.98649499054740353),
 ('warm', 0.98485340331145166),
 ('ages', 0.98449898190068863),
 ('guilt', 0.98082925301172619),
 ('glover', 0.98082925301172619),
 ('carrey', 0.98082925301172619),
 ('learns', 0.97881108885548895),
 ('unusual', 0.97788374278196932),
 ('sons', 0.97777581552483595),
 ('complex', 0.97761897738147796),
 ('essence', 0.97753435711487369),
 ('brazil', 0.9769153536905899),
 ('widow', 0.97650959186720987),
 ('solid', 0.97537964824416146),
 ('beautiful', 0.97326301262841053),
 ('holmes', 0.97246100334120955),
 ('awe', 0.97186058302896583),
 ('vhs', 0.97116734209998934),
 ('eerie', 0.97116734209998934),
 ('lonely', 0.96873720724669754),
 ('grim', 0.96873720724669754),
 ('sport', 0.96825047080486615),
 ('debut', 0.96508089604358704),
 ('destiny', 0.96343751029985703),
 ('thrillers', 0.96281074750904794),
 ('tears', 0.95977584381389391),
 ('rose', 0.95664202739772253),
 ('feelings', 0.95551144502743635),
 ('ginger', 0.95551144502743635),
 ('winning', 0.95471810900804055),
 ('stanley', 0.95387344302319799),
 ('cox', 0.95343027882361187),
 ('paris', 0.95278479030472663),
 ('heart', 0.95238806924516806),
 ('hooked', 0.95155887071161305),
 ('comfortable', 0.94803943018873538),
 ('mgm', 0.94446160884085151),
 ('masterpiece', 0.94155039863339296),
 ('themes', 0.94118828349588235),
 ('danny', 0.93967118051821874),
 ('anime', 0.93378388932167222),
 ('perry', 0.93328830824272613),
 ('joy', 0.93301752567946861),
 ('lovable', 0.93081883243706487),
 ('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),
 ('treat', 0.91193342650519937),
 ('subsequent', 0.91056005716517008),
 ('kennedy', 0.90981821736853763),
 ('loving', 0.90967549275543591),
 ('surprising', 0.90937028902958128),
 ('quiet', 0.90648673177753425),
 ('winter', 0.90624039602065365),
 ('reveals', 0.90490540964902977),
 ('raw', 0.90445627422715225),
 ('funniest', 0.90078654533818991),
 ('pleased', 0.89994159387262562),
 ('norman', 0.89994159387262562),
 ('thief', 0.89874642222324552),
 ('season', 0.89827222637147675),
 ('secrets', 0.89794159320595857),
 ('colorful', 0.89705936994626756),
 ('highest', 0.8967461358011849),
 ('compelling', 0.89462923509297576),
 ('danes', 0.89248008318043659),
 ('castle', 0.88967708335606499),
 ('kudos', 0.88889175768604067),
 ('great', 0.88810470901464589),
 ('baseball', 0.88730319500090271),
 ('subtitles', 0.88730319500090271),
 ('bleak', 0.88730319500090271),
 ('winner', 0.88643776872447388),
 ('tragedy', 0.88563699078315261),
 ('todd', 0.88551907320740142),
 ('nicely', 0.87924946019380601),
 ('arthur', 0.87546873735389985),
 ('essential', 0.87373111745535925),
 ('gorgeous', 0.8731725250935497),
 ('fonda', 0.87294029100054127),
 ('eastwood', 0.87139541196626402),
 ('focuses', 0.87082835779739776),
 ('enjoyed', 0.87070195951624607),
 ('natural', 0.86997924506912838),
 ('intensity', 0.86835126958503595),
 ('witty', 0.86824103423244681),
 ('rob', 0.8642954367557748),
 ('worlds', 0.86377269759070874),
 ('health', 0.86113891179907498),
 ('magical', 0.85953791528170564),
 ('deeper', 0.85802182375017932),
 ('lucy', 0.85618680780444956),
 ('moving', 0.85566611005772031),
 ('lovely', 0.85290640004681306),
 ('purple', 0.8513711857748395),
 ('memorable', 0.84801189112086062),
 ('sings', 0.84729786038720367),
 ('craig', 0.84342938360928321),
 ('modesty', 0.84342938360928321),
 ('relate', 0.84326559685926517),
 ('episodes', 0.84223712084137292),
 ('strong', 0.84167135777060931),
 ('smith', 0.83959811108590054),
 ('tear', 0.83704136022001441),
 ('apartment', 0.83333115290549531),
 ('princess', 0.83290912293510388),
 ('disagree', 0.83290912293510388),
 ('kung', 0.83173334384609199),
 ('adventure', 0.83150561393278388),
 ('columbo', 0.82667857318446791),
 ('jake', 0.82667857318446791),
 ('adds', 0.82485652591452319),
 ('hart', 0.82472353834866463),
 ('strength', 0.82417544296634937),
 ('realizes', 0.82360006895738058),
 ('dave', 0.8232003088081431),
 ('childhood', 0.82208086393583857),
 ('forbidden', 0.81989888619908913),
 ('tight', 0.81883539572344199),
 ('surreal', 0.8178506590609026),
 ('manager', 0.81770990320170756),
 ('dancer', 0.81574950265227764),
 ('studios', 0.81093021621632877),
 ('con', 0.81093021621632877),
 ('miike', 0.80821651034473263),
 ('realistic', 0.80807714723392232),
 ('explicit', 0.80792269515237358),
 ('kurt', 0.8060875917405409),
 ('traditional', 0.80535917116687328),
 ('deals', 0.80535917116687328),
 ('holds', 0.80493858654806194),
 ('carl', 0.80437281567016972),
 ('touches', 0.80396154690023547),
 ('gene', 0.80314807577427383),
 ('albert', 0.8027669055771679),
 ('abc', 0.80234647252493729),
 ('cry', 0.80011930011211307),
 ('sides', 0.7995275841185171),
 ('develops', 0.79850769621777162),
 ('eyre', 0.79850769621777162),
 ('dances', 0.79694397424158891),
 ('oscars', 0.79633141679517616),
 ('legendary', 0.79600456599965308),
 ('hearted', 0.79492987486988764),
 ('importance', 0.79492987486988764),
 ('portraying', 0.79356592830699269),
 ('impressed', 0.79258107754813223),
 ('waters', 0.79112758892014912),
 ('empire', 0.79078565012386137),
 ('edge', 0.789774016249017),
 ('jean', 0.78845736036427028),
 ('environment', 0.78845736036427028),
 ('sentimental', 0.7864791203521645),
 ('captured', 0.78623760362595729),
 ('styles', 0.78592891401091158),
 ('daring', 0.78592891401091158),
 ('frank', 0.78275933924963248),
 ('tense', 0.78275933924963248),
 ('backgrounds', 0.78275933924963248),
 ('matches', 0.78275933924963248),
 ('gothic', 0.78209466657644144),
 ('sharp', 0.7814397877056235),
 ('achieved', 0.78015855754957497),
 ('court', 0.77947526404844247),
 ('steals', 0.7789140023173704),
 ('rules', 0.77844476107184035),
 ('colors', 0.77684619943659217),
 ('reunion', 0.77318988823348167),
 ('covers', 0.77139937745969345),
 ('tale', 0.77010822169607374),
 ('rain', 0.7683706017975328),
 ('denzel', 0.76804848873306297),
 ('stays', 0.76787072675588186),
 ('blob', 0.76725515271366718),
 ('maria', 0.76214005204689672),
 ('conventional', 0.76214005204689672),
 ('fresh', 0.76158434211317383),
 ('midnight', 0.76096977689870637),
 ('landscape', 0.75852993982279704),
 ('animated', 0.75768570169751648),
 ('titanic', 0.75666058628227129),
 ('sunday', 0.75666058628227129),
 ('spring', 0.7537718023763802),
 ('cagney', 0.7537718023763802),
 ('enjoyable', 0.75246375771636476),
 ('immensely', 0.75198768058287868),
 ('sir', 0.7507762933965817),
 ('nevertheless', 0.75067102469813185),
 ('driven', 0.74994477895307854),
 ('performances', 0.74883252516063137),
 ('memories', 0.74721440183022114),
 ('nowadays', 0.74721440183022114),
 ('simple', 0.74641420974143258),
 ('golden', 0.74533293373051557),
 ('leslie', 0.74533293373051557),
 ('lovers', 0.74497224842453125),
 ('relationship', 0.74484232345601786),
 ('supporting', 0.74357803418683721),
 ('che', 0.74262723782331497),
 ('packed', 0.7410032017375805),
 ('trek', 0.74021469141793106),
 ('provoking', 0.73840377214806618),
 ('strikes', 0.73759894313077912),
 ('depiction', 0.73682224406260699),
 ('emotional', 0.73678211645681524),
 ('secretary', 0.7366322924996842),
 ('influenced', 0.73511137965897755),
 ('florida', 0.73511137965897755),
 ('germany', 0.73288750920945944),
 ('brings', 0.73142936713096229),
 ('lewis', 0.73129894652432159),
 ('elderly', 0.73088750854279239),
 ('owner', 0.72743625403857748),
 ('streets', 0.72666987259858895),
 ('henry', 0.72642196944481741),
 ('portrays', 0.72593700338293632),
 ('bears', 0.7252354951114458),
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 ('caught', 0.44610275383999071),
 ('hamlet', 0.44558510189758965),
 ('chinese', 0.44507424620321018),
 ('welcome', 0.44438052435783792),
 ('birth', 0.44368632092836219),
 ('represents', 0.44320543609101143),
 ('puts', 0.44279106572085081),
 ('visuals', 0.44183275227903923),
 ('fame', 0.44183275227903923),
 ('closer', 0.44183275227903923),
 ('web', 0.44183275227903923),
 ('criminal', 0.4412745608048752),
 ('minor', 0.4409224199448939),
 ('jon', 0.44086703515908027),
 ('liked', 0.44074991514020723),
 ('restaurant', 0.44031183943833246),
 ('de', 0.43983275161237217),
 ('flaws', 0.43983275161237217),
 ('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)]

Transforming Text into Numbers


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]:

Project 2: Creating the Input/Output Data


In [16]:
vocab = set(total_counts.keys())
vocab_size = len(vocab)
print(vocab_size)


74074

In [17]:
list(vocab)


Out[17]:
['',
 'pot',
 'outback',
 'reaffirms',
 'recriminations',
 'overboard',
 'pube',
 'yue',
 'fairborn',
 'mashing',
 'hyper',
 'cadilac',
 'hagan',
 'southside',
 'des',
 'detraction',
 'inconnu',
 'creatures',
 'selves',
 'kliegs',
 'coquettish',
 'unrealistic',
 'primed',
 'horndogging',
 'intrusive',
 'formate',
 'merest',
 'thimig',
 'tiangle',
 'purifier',
 'caleigh',
 'unconfident',
 'cahulawassee',
 'urbania',
 'sundownthe',
 'psychos',
 'sheeba',
 'lashing',
 'flavoring',
 'metric',
 'wheedle',
 'compiled',
 'enoch',
 'dotty',
 'peep',
 'unethical',
 'luminously',
 'sloppier',
 'syllabic',
 'diffuses',
 'kharis',
 'kitrosser',
 'uh',
 'pornography',
 'nutjobseen',
 'lurk',
 'ralphie',
 'redo',
 'shuddup',
 'largest',
 'calms',
 'idyll',
 'katre',
 'variant',
 'unscience',
 'britt',
 'nicotine',
 'complementary',
 'tossing',
 'chandrmukhi',
 'bleakest',
 'remixed',
 'karts',
 'defibulator',
 'nubes',
 'puritanical',
 'ctomvelu',
 'garsh',
 'evaluate',
 'faridany',
 'bourn',
 'labourers',
 'brunna',
 'empurpled',
 'dramatical',
 'elucubrate',
 'kidnap',
 'patricide',
 'demi',
 'cleaning',
 'renard',
 'impersonator',
 'huttner',
 'redcoat',
 'kiddos',
 'ordeal',
 'regal',
 'satish',
 'privacy',
 'unburdened',
 'volts',
 'silents',
 'ghim',
 'accordance',
 'morricones',
 'smorsgabord',
 'azema',
 'economists',
 'nerdy',
 'repaired',
 'godzillasaurus',
 'finese',
 'wsj',
 'awstruck',
 'paccino',
 'instigated',
 'bladrick',
 'rinaldo',
 'badman',
 'universial',
 'casing',
 'propagandic',
 'roshan',
 'crowd',
 'gallaghers',
 'temps',
 'thumbes',
 'signed',
 'piece',
 'whiteclad',
 'deepening',
 'multicolored',
 'atomspheric',
 'reckons',
 'kodak',
 'lancre',
 'ravens',
 'conferred',
 'ritualistic',
 'unmelodious',
 'gena',
 'anurag',
 'rue',
 'frogging',
 'penthouse',
 'jones',
 'brotherhood',
 'gradually',
 'bounce',
 'burlesqued',
 'rubell',
 'treen',
 'battleground',
 'eddi',
 'rebe',
 'romans',
 'mellon',
 'inconvenienced',
 'plastique',
 'zering',
 'rockies',
 'burr',
 'disillusion',
 'upwards',
 'mollified',
 'springy',
 'valli',
 'undernourished',
 'jugde',
 'crewmate',
 'butterface',
 'mills',
 'charlton',
 'historyish',
 'gabfests',
 'choy',
 'libertine',
 'open',
 'exciting',
 'rubano',
 'naschy',
 'inspects',
 'hm',
 'hipness',
 'chesticles',
 'mannequins',
 'mindgames',
 'wished',
 'eyeliner',
 'prosero',
 'fallowing',
 'dreamland',
 'default',
 'counterpointing',
 'meatheads',
 'soiree',
 'wounded',
 'berardinelli',
 'bunk',
 'vandenberg',
 'nyberg',
 'layla',
 'outraged',
 'signboard',
 'chickboxer',
 'lorraine',
 'reeducation',
 'satiating',
 'evading',
 'feu',
 'blurring',
 'hhoorriibbllee',
 'messier',
 'trinket',
 'stunts',
 'lighter',
 'reenactments',
 'sneaking',
 'vagueness',
 'equalling',
 'try',
 'stirrings',
 'fictitional',
 'upbeat',
 'djjohn',
 'metroid',
 'premise',
 'thumping',
 'fancifully',
 'presages',
 'asiaphile',
 'campily',
 'sk',
 'teething',
 'shaye',
 'erwin',
 'learnfrom',
 'arnim',
 'nose',
 'campell',
 'skyraiders',
 'exiting',
 'hlots',
 'gallops',
 'involve',
 'hypnotically',
 'sycophant',
 'odder',
 'camila',
 'vrsel',
 'incense',
 'lagaan',
 'metzger',
 'propsdodgy',
 'napkins',
 'papamoskou',
 'oo',
 'autistic',
 'godby',
 'plonking',
 'shoots',
 'towers',
 'ladened',
 'sala',
 'sindbad',
 'stuyvesant',
 'baldrick',
 'pursuing',
 'physique',
 'kana',
 'modulate',
 'groping',
 'manghattan',
 'jelled',
 'values',
 'picky',
 'matured',
 'sanctifying',
 'kryptonite',
 'aonn',
 'outbreaking',
 'dunns',
 'porfirio',
 'mikl',
 'summerisle',
 'negrophile',
 'baggins',
 'wmaq',
 'conspiracy',
 'yakusho',
 'veto',
 'meditative',
 'freshest',
 'positioning',
 'campfire',
 'gassed',
 'mixed',
 'sookie',
 'pfifer',
 'mott',
 'impatiently',
 'spools',
 'leit',
 'meningitis',
 'theotocopulos',
 'warnning',
 'tilly',
 'trini',
 'spatially',
 'adversely',
 'ality',
 'lethargically',
 'lembach',
 'wounder',
 'impeccable',
 'barantini',
 'intransigent',
 'differences',
 'beth',
 'approach',
 'derring',
 'bewitching',
 'commentators',
 'protagoness',
 'set',
 'aloysius',
 'bernard',
 'transmitter',
 'heyward',
 'reservoir',
 'seized',
 'divorces',
 'anguish',
 'servings',
 'wantabedde',
 'teddi',
 'specifies',
 'drum',
 'cased',
 'cambodian',
 'hearsay',
 'meditate',
 'sitka',
 'benson',
 'mrs',
 'decorous',
 'mclaglen',
 'vacuous',
 'smelly',
 'neurotoxic',
 'pierre',
 'carrys',
 'preoccupation',
 'subsidy',
 'dimas',
 'precociousness',
 'faves',
 'hefty',
 'herding',
 'dantesque',
 'faye',
 'pistols',
 'affectations',
 'flirting',
 'montazuma',
 'summation',
 'noblemen',
 'naturalizing',
 'seuss',
 'stygian',
 'maclachalan',
 'gaslight',
 'carradine',
 'cavemen',
 'southron',
 'archipelago',
 'cloutish',
 'calcifying',
 'jennifers',
 'candid',
 'cartwheels',
 'hellyou',
 'silo',
 'celeb',
 'horts',
 'ailment',
 'expensive',
 'futilely',
 'quinton',
 'bonds',
 'penetrate',
 'accentuate',
 'vanning',
 'mothballed',
 'decorate',
 'immobilize',
 'unmated',
 'netflix',
 'goaul',
 'immaterial',
 'rejoiced',
 'guerra',
 'gamboling',
 'hatching',
 'guiding',
 'dispatcher',
 'negotiating',
 'limousines',
 'journos',
 'moseys',
 'jailhouse',
 'singaporean',
 'thimothy',
 'astra',
 'decorating',
 'highlights',
 'narrowly',
 'casual',
 'infiltration',
 'habit',
 'respondents',
 'evacuee',
 'transfuse',
 'lavant',
 'considers',
 'externally',
 'computers',
 'herrmann',
 'myerson',
 'bratwurst',
 'companys',
 'orgolini',
 'watase',
 'midkiff',
 'triad',
 'baiscally',
 'arrivals',
 'forceful',
 'tidying',
 'magnficiant',
 'jogging',
 'profession',
 'emphasised',
 'pee',
 'lional',
 'natwick',
 'canuck',
 'tasteful',
 'giudizio',
 'patrizio',
 'shantytowns',
 'maniac',
 'bibbidi',
 'sponge',
 'diaper',
 'hedgehog',
 'placate',
 'lustily',
 'kilograms',
 'hedrin',
 'poundage',
 'germs',
 'gramm',
 'gillian',
 'leaver',
 'whoville',
 'baccalaurat',
 'kremlin',
 'invigorates',
 'hater',
 'brazen',
 'vapor',
 'trelkovski',
 'spurred',
 'ib',
 'nghya',
 'dithering',
 'affective',
 'crated',
 'payout',
 'rexas',
 'select',
 'withdrawn',
 'utilizing',
 'gerrard',
 'complexities',
 'reality',
 'faculty',
 'tedium',
 'disintegrating',
 'ur',
 'unrequited',
 'strumming',
 'purest',
 'divinity',
 'newmar',
 'aggrivating',
 'acheived',
 'ryszard',
 'macabra',
 'photographic',
 'bling',
 'paneled',
 'molla',
 'guidelines',
 'thwarted',
 'profiteering',
 'zuni',
 'fierceness',
 'womanize',
 'plessis',
 'arteries',
 'wanky',
 'freeing',
 'unleash',
 'kebab',
 'cursorily',
 'tin',
 'accommodations',
 'clarinet',
 'puddles',
 'healthier',
 'beleive',
 'sedates',
 'walters',
 'jarman',
 'barack',
 'surrah',
 'antiques',
 'accidence',
 'missie',
 'qustions',
 'cretin',
 'nelligan',
 'allo',
 'coinciding',
 'himesh',
 'labyrinth',
 'cheekily',
 'overscaled',
 'sprouts',
 'hahah',
 'monstro',
 'flu',
 'pleeeease',
 'numerical',
 'warfield',
 'corporations',
 'clanky',
 'vats',
 'soha',
 'otoko',
 'overrule',
 'praying',
 'untainted',
 'hailstorm',
 'figga',
 'propagandizing',
 'sappy',
 'daffily',
 'concept',
 'crepe',
 'johnstone',
 'yor',
 'muddy',
 'star',
 'guntenberg',
 'karas',
 'duda',
 'amusing',
 'isao',
 'waffling',
 'horrors',
 'tighty',
 'adverse',
 'stephan',
 'borowczyk',
 'tsau',
 'tamerlane',
 'spectacle',
 'cuisine',
 'brulier',
 'haven',
 'enlivened',
 'supurrrrb',
 'trudie',
 'zealousness',
 'ointment',
 'courtrooms',
 'campions',
 'clasping',
 'transitions',
 'squares',
 'purge',
 'bison',
 'groggy',
 'polished',
 'tsunami',
 'toreton',
 'pill',
 'engalnd',
 'posy',
 'harburg',
 'jonbenet',
 'widowed',
 'form',
 'geoffrey',
 'consumingly',
 'codeine',
 'mobilized',
 'dismember',
 'nineveh',
 'predictable',
 'centerline',
 'sow',
 'blowtorch',
 'jaclyn',
 'gunshots',
 'woulda',
 'penguim',
 'ammmmm',
 'lehch',
 'shallow',
 'rin',
 'sarte',
 'jin',
 'groundbraking',
 'neenan',
 'serpentine',
 'vacuity',
 'resist',
 'guerillas',
 'felonies',
 'filmi',
 'nez',
 'blalack',
 'impaling',
 'unrecognisable',
 'pickin',
 'lure',
 'titles',
 'suitcases',
 'zombielike',
 'security',
 'caricatured',
 'arnies',
 'feverishly',
 'nyfiken',
 'ceramics',
 'unik',
 'posturing',
 'camion',
 'lololol',
 'verbose',
 'unsurpassed',
 'predigested',
 'malformations',
 'thi',
 'jyotika',
 'fakey',
 'incapacitates',
 'misogynistic',
 'keyboards',
 'acetylene',
 'stamper',
 'tech',
 'overcame',
 'prayer',
 'lid',
 'skimmed',
 'overplayed',
 'whistling',
 'amps',
 'telegraphing',
 'mewing',
 'playboy',
 'camouflaged',
 'knicker',
 'mistrustful',
 'roundup',
 'fifths',
 'militarized',
 'outsides',
 'mostey',
 'sew',
 'expressionism',
 'tailored',
 'careered',
 'brie',
 'meercats',
 'snail',
 'cheeseburgers',
 'misfortune',
 'controls',
 'exalted',
 'disseminated',
 'meysels',
 'canonical',
 'seers',
 'racking',
 'collide',
 'sculpture',
 'illuminators',
 'stimuli',
 'agustin',
 'frontyard',
 'ghoulish',
 'dennehy',
 'wrongdoings',
 'whiner',
 'objections',
 'gawkers',
 'capomezza',
 'pff',
 'athena',
 'pumpkins',
 'jaja',
 'unfound',
 'heavily',
 'pookie',
 'precept',
 'dunny',
 'saranadon',
 'dirtiest',
 'adle',
 'sporting',
 'jot',
 'kuchler',
 'colorfully',
 'alesia',
 'horridly',
 'horshack',
 'surpasses',
 'hangings',
 'ruggedly',
 'refute',
 'ronet',
 'chandleresque',
 'matiss',
 'suceed',
 'intermittently',
 'makeover',
 'blachere',
 'thawing',
 'sofas',
 'lout',
 'devour',
 'acquaintaces',
 'boulanger',
 'build',
 'plains',
 'deliveries',
 'elixirs',
 'beards',
 'woodcourt',
 'over',
 'tel',
 'talmud',
 'mads',
 'doubting',
 'barjatyagot',
 'clich',
 'construed',
 'holdouts',
 'psychotically',
 'sleeps',
 'graders',
 'profundity',
 'polito',
 'anthropomorphic',
 'denouement',
 'rainers',
 'pampered',
 'kazumi',
 'tami',
 'hadleys',
 'mcvay',
 'gauge',
 'squatting',
 'circuited',
 'toys',
 'controlness',
 'moti',
 'descript',
 'blanche',
 'sleazebag',
 'happy',
 'borat',
 'spewing',
 'lucia',
 'totalitarian',
 'hadda',
 'henshaw',
 'jone',
 'expectant',
 'deille',
 'flared',
 'heartbreaking',
 'achingly',
 'misdemeanors',
 'relationship',
 'yomiuri',
 'programme',
 'defy',
 'chagrined',
 'disinformation',
 'alwina',
 'sudow',
 'screenwriter',
 'spats',
 'christansan',
 'adulteries',
 'islands',
 'cinmas',
 'kinnair',
 'externalities',
 'sikking',
 'pouch',
 'thid',
 'hollywierd',
 'renting',
 'kidnapper',
 'biangle',
 'nail',
 'gundams',
 'milhalovitch',
 'dedication',
 'schlocky',
 'tinge',
 'lr',
 'flyers',
 'embezzler',
 'enticing',
 'steadier',
 'thud',
 'eaton',
 'moderation',
 'juicier',
 'jettisoned',
 'bebe',
 'callipygian',
 'jumbo',
 'adjusts',
 'selective',
 'fu',
 'smithdale',
 'flowerchild',
 'newpaper',
 'column',
 'judah',
 'automag',
 'cannons',
 'avon',
 'medichlorians',
 'lever',
 'patient',
 'kicha',
 'fetuses',
 'insufficiency',
 'matrix',
 'chastised',
 'ewwww',
 'receives',
 'peninsular',
 'boundless',
 'afrovideo',
 'lifecycle',
 'organ',
 'muddling',
 'sacrilegious',
 'grasshopper',
 'nallae',
 'torchon',
 'nikopol',
 'rifkin',
 'cowgirl',
 'misfigured',
 'soiler',
 'mindless',
 'maccullum',
 'gaionsbourg',
 'treasured',
 'thesigner',
 'lighted',
 'sunniness',
 'taverns',
 'hrithek',
 'fangirls',
 'slopped',
 'volume',
 'romeo',
 'sensibilities',
 'snowbeast',
 'crisi',
 'malebranche',
 'balloon',
 'pawing',
 'bacall',
 'avoiding',
 'forbids',
 'produce',
 'finney',
 'physiques',
 'screwy',
 'troublemaker',
 'unbeatableinspired',
 'shihomi',
 'jasna',
 'ae',
 'safiya',
 'thurig',
 'dewames',
 'refreshes',
 'abusers',
 'pilling',
 'fission',
 'yaaay',
 'paterfamilias',
 'nandani',
 'smartaleck',
 'appliance',
 'cowley',
 'belen',
 'trusted',
 'almora',
 'bogglingly',
 'waive',
 'crappily',
 'mama',
 'wotw',
 'brak',
 'supermoral',
 'malignancy',
 'lan',
 'validation',
 'edwina',
 'bocanegra',
 'suoi',
 'jodoworsky',
 'mauricio',
 'stubbed',
 'gateshead',
 'buuel',
 'brazlia',
 'unperturbed',
 'seizes',
 'divali',
 'crissakes',
 'slowdown',
 'zeland',
 'blowtorches',
 'isabelwho',
 'chawala',
 'indicating',
 'danson',
 'dyslexic',
 'fitz',
 'nickelodeon',
 'andi',
 'bravado',
 'braggin',
 'compatibility',
 'sakal',
 'euphemistic',
 'recommendations',
 'radiantly',
 'mexico',
 'malaga',
 'bharat',
 'neurologist',
 'trifle',
 'discounted',
 'meshed',
 'crossbows',
 'condones',
 'dodger',
 'dens',
 'interrupt',
 'shouldn',
 'boilerplate',
 'cameos',
 'ho',
 'wangle',
 'righting',
 'churn',
 'ithaca',
 'hodges',
 'fitzpatrick',
 'caveman',
 'quelle',
 'dat',
 'chamberlin',
 'robin',
 'overturn',
 'toddlers',
 'penalty',
 'stimulated',
 'breakaway',
 'contaminating',
 'fluid',
 ...]

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,
 'pot': 1,
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 'malignancy': 932,
 'lan': 933,
 'validation': 934,
 'edwina': 935,
 'bocanegra': 936,
 'suoi': 937,
 'jodoworsky': 938,
 'mauricio': 939,
 'stubbed': 940,
 'gateshead': 941,
 'buuel': 942,
 'brazlia': 943,
 'unperturbed': 944,
 'seizes': 945,
 'divali': 946,
 'crissakes': 947,
 'slowdown': 948,
 'zeland': 949,
 'blowtorches': 950,
 'isabelwho': 951,
 'chawala': 952,
 'indicating': 953,
 'danson': 954,
 'dyslexic': 955,
 'fitz': 956,
 'nickelodeon': 957,
 'andi': 958,
 'bravado': 959,
 'braggin': 960,
 'compatibility': 961,
 'sakal': 962,
 'euphemistic': 963,
 'recommendations': 964,
 'radiantly': 965,
 'mexico': 966,
 'malaga': 967,
 'bharat': 968,
 'neurologist': 969,
 'trifle': 970,
 'discounted': 971,
 'meshed': 972,
 'crossbows': 973,
 'condones': 974,
 'dodger': 975,
 'dens': 976,
 'interrupt': 977,
 'shouldn': 978,
 'boilerplate': 979,
 'cameos': 980,
 'ho': 981,
 'wangle': 982,
 'righting': 983,
 'churn': 984,
 'ithaca': 985,
 'hodges': 986,
 'fitzpatrick': 987,
 'caveman': 988,
 'quelle': 989,
 'dat': 990,
 'chamberlin': 991,
 'robin': 992,
 'overturn': 993,
 'toddlers': 994,
 'penalty': 995,
 'stimulated': 996,
 'breakaway': 997,
 'contaminating': 998,
 'fluid': 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

Project 3: Building a Neural Network

  • Start with your neural network from the last chapter
  • 3 layer neural network
  • no non-linearity in hidden layer
  • use our functions to create the training data
  • create a "pre_process_data" function to create vocabulary for our training data generating functions
  • modify "train" to train over the entire corpus

Where to Get Help if You Need it


In [28]:
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 [30]:
# evaluate our model before training (just to show how horrible it is)
mlp.test(reviews[-1000:],labels[-1000:])


Progress:99.9% Speed(reviews/sec):454.5% #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: 

Understanding Neural Noise


In [31]:
from IPython.display import Image
Image(filename='sentiment_network.png')


Out[31]:

In [70]:
def update_input_layer(review):
    
    global layer_0
    
    # clear out previous state, reset the layer to be all 0s
    layer_0 *= 0
    for word in review.split(" "):
        layer_0[0][word2index[word]] += 1

update_input_layer(reviews[0])

In [71]:
layer_0


Out[71]:
array([[ 18.,   0.,   0., ...,   0.,   0.,   0.]])

In [79]:
review_counter = Counter()

In [80]:
for word in reviews[0].split(" "):
    review_counter[word] += 1

In [81]:
review_counter.most_common()


Out[81]:
[('.', 27),
 ('', 18),
 ('the', 9),
 ('to', 6),
 ('i', 5),
 ('high', 5),
 ('is', 4),
 ('of', 4),
 ('a', 4),
 ('bromwell', 4),
 ('teachers', 4),
 ('that', 4),
 ('their', 2),
 ('my', 2),
 ('at', 2),
 ('as', 2),
 ('me', 2),
 ('in', 2),
 ('students', 2),
 ('it', 2),
 ('student', 2),
 ('school', 2),
 ('through', 1),
 ('insightful', 1),
 ('ran', 1),
 ('years', 1),
 ('here', 1),
 ('episode', 1),
 ('reality', 1),
 ('what', 1),
 ('far', 1),
 ('t', 1),
 ('saw', 1),
 ('s', 1),
 ('repeatedly', 1),
 ('isn', 1),
 ('closer', 1),
 ('and', 1),
 ('fetched', 1),
 ('remind', 1),
 ('can', 1),
 ('welcome', 1),
 ('line', 1),
 ('your', 1),
 ('survive', 1),
 ('teaching', 1),
 ('satire', 1),
 ('classic', 1),
 ('who', 1),
 ('age', 1),
 ('knew', 1),
 ('schools', 1),
 ('inspector', 1),
 ('comedy', 1),
 ('down', 1),
 ('about', 1),
 ('pity', 1),
 ('m', 1),
 ('all', 1),
 ('adults', 1),
 ('see', 1),
 ('think', 1),
 ('situation', 1),
 ('time', 1),
 ('pomp', 1),
 ('lead', 1),
 ('other', 1),
 ('much', 1),
 ('many', 1),
 ('which', 1),
 ('one', 1),
 ('profession', 1),
 ('programs', 1),
 ('same', 1),
 ('some', 1),
 ('such', 1),
 ('pettiness', 1),
 ('immediately', 1),
 ('expect', 1),
 ('financially', 1),
 ('recalled', 1),
 ('tried', 1),
 ('whole', 1),
 ('right', 1),
 ('life', 1),
 ('cartoon', 1),
 ('scramble', 1),
 ('sack', 1),
 ('believe', 1),
 ('when', 1),
 ('than', 1),
 ('burn', 1),
 ('pathetic', 1)]

Project 4: Reducing Noise in our Input Data


In [32]:
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 [83]:
mlp = SentimentNetwork(reviews[:-1000],labels[:-1000], learning_rate=0.1)

In [84]:
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):91.50 #Correct:1795 #Trained:2501 Training Accuracy:71.7%
Progress:20.8% Speed(reviews/sec):95.25 #Correct:3811 #Trained:5001 Training Accuracy:76.2%
Progress:31.2% Speed(reviews/sec):93.74 #Correct:5898 #Trained:7501 Training Accuracy:78.6%
Progress:41.6% Speed(reviews/sec):93.69 #Correct:8042 #Trained:10001 Training Accuracy:80.4%
Progress:52.0% Speed(reviews/sec):95.27 #Correct:10186 #Trained:12501 Training Accuracy:81.4%
Progress:62.5% Speed(reviews/sec):98.19 #Correct:12317 #Trained:15001 Training Accuracy:82.1%
Progress:72.9% Speed(reviews/sec):98.56 #Correct:14440 #Trained:17501 Training Accuracy:82.5%
Progress:83.3% Speed(reviews/sec):99.74 #Correct:16613 #Trained:20001 Training Accuracy:83.0%
Progress:93.7% Speed(reviews/sec):100.7 #Correct:18794 #Trained:22501 Training Accuracy:83.5%
Progress:99.9% Speed(reviews/sec):101.9 #Correct:20115 #Trained:24000 Training Accuracy:83.8%

In [85]:
# evaluate our model before training (just to show how horrible it is)
mlp.test(reviews[-1000:],labels[-1000:])


Progress:99.9% Speed(reviews/sec):832.7% #Correct:851 #Tested:1000 Testing Accuracy:85.1%

Analyzing Inefficiencies in our Network


In [88]:
Image(filename='sentiment_network_sparse.png')


Out[88]:

In [89]:
layer_0 = np.zeros(10)

In [90]:
layer_0


Out[90]:
array([ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.])

In [91]:
layer_0[4] = 1
layer_0[9] = 1

In [92]:
layer_0


Out[92]:
array([ 0.,  0.,  0.,  0.,  1.,  0.,  0.,  0.,  0.,  1.])

In [93]:
weights_0_1 = np.random.randn(10,5)

In [94]:
layer_0.dot(weights_0_1)


Out[94]:
array([-0.10503756,  0.44222989,  0.24392938, -0.55961832,  0.21389503])

In [101]:
indices = [4,9]

In [102]:
layer_1 = np.zeros(5)

In [103]:
for index in indices:
    layer_1 += (weights_0_1[index])

In [104]:
layer_1


Out[104]:
array([-0.10503756,  0.44222989,  0.24392938, -0.55961832,  0.21389503])

In [33]:
Image(filename='sentiment_network_sparse_2.png')


Out[33]:

In [68]:
import time
import sys

# 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):
       
        np.random.seed(1)
    
        self.pre_process_data(reviews)
        
        self.init_network(len(self.review_vocab),hidden_nodes, 1, learning_rate)
        
        
    def pre_process_data(self,reviews):
        
        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))
        self.layer_1 = np.zeros((1,hidden_nodes))
        
    def sigmoid(self,x):
        return 1 / (1 + np.exp(-x))
    
    
    def sigmoid_output_2_derivative(self,output):
        return output * (1 - output)
    
    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(" "):
            self.layer_0[0][self.word2index[word]] = 1

    def get_target_for_label(self,label):
        if(label == 'POSITIVE'):
            return 1
        else:
            return 0
        
    def train(self, training_reviews_raw, training_labels):
        
        training_reviews = list()
        for review in training_reviews_raw:
            indices = set()
            for word in review.split(" "):
                if(word in self.word2index.keys()):
                    indices.add(self.word2index[word])
            training_reviews.append(list(indices))
        
        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

            # Hidden layer
#             layer_1 = self.layer_0.dot(self.weights_0_1)
            self.layer_1 *= 0
            for index in review:
                self.layer_1 += self.weights_0_1[index]
            
            # Output layer
            layer_2 = self.sigmoid(self.layer_1.dot(self.weights_1_2))

            #### Implement the backward pass here ####
            ### Backward pass ###

            # 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)

            # 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

            # Update the weights
            self.weights_1_2 -= self.layer_1.T.dot(layer_2_delta) * self.learning_rate # update hidden-to-output weights with gradient descent step
            
            for index in review:
                self.weights_0_1[index] -= layer_1_delta[0] * 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] + "%")
        
    
    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


        # Hidden layer
        self.layer_1 *= 0
        unique_indices = set()
        for word in review.lower().split(" "):
            if word in self.word2index.keys():
                unique_indices.add(self.word2index[word])
        for index in unique_indices:
            self.layer_1 += self.weights_0_1[index]
        
        # Output layer
        layer_2 = self.sigmoid(self.layer_1.dot(self.weights_1_2))
        
        if(layer_2[0] > 0.5):
            return "POSITIVE"
        else:
            return "NEGATIVE"

In [69]:
mlp = SentimentNetwork(reviews[:-1000],labels[:-1000], learning_rate=0.1)

In [70]:
mlp.train(reviews[:-1000],labels[:-1000])


Progress:99.9% Speed(reviews/sec):675.0 #Correct:20141 #Trained:24000 Training Accuracy:83.9%

In [ ]:


In [ ]: