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

### What You Should Already Know

• neural networks, forward and back-propagation
• 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 [12]:

def pretty_print_review_and_label(i):
print(labels[i] + "\t:\t" + reviews[i][:80] + "...")

g = open('reviews.txt','r') # What we know!
g.close()

g = open('labels.txt','r') # What we WANT to know!
g.close()

``````
``````

In [13]:

len(reviews)

``````
``````

Out[13]:

25000

``````
``````

In [14]:

reviews[0]

``````
``````

Out[14]:

'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 [15]:

labels[0]

``````
``````

Out[15]:

'POSITIVE'

``````

# Lesson: Develop a Predictive Theory

``````

In [16]:

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 [17]:

from collections import Counter
import numpy as np

``````
``````

In [18]:

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

``````
``````

In [19]:

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 [20]:

positive_counts.most_common()

``````
``````

Out[20]:

[('', 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),
('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),
('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),
('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),
('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),
('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),
('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),
('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),
('experience', 642),
('eyes', 641),
('sex', 638),
('direction', 637),
('called', 637),
('directed', 636),
('lines', 634),
('behind', 633),
('sort', 632),
('actress', 631),
('oscar', 628),
('including', 627),
('example', 627),
('known', 625),
('musical', 625),
('chance', 621),
('score', 620),
('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),
('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),
('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),
('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),
('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),
('general', 383),
('sequences', 383),
('lee', 383),
('points', 382),
('earlier', 382),
('gone', 379),
('check', 379),
('suspense', 378),
('recommended', 378),
('ten', 378),
('third', 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),
('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),
('wouldn', 361),
('straight', 361),
('features', 361),
('forget', 360),
('setting', 360),
('lack', 360),
('married', 359),
('mark', 359),
('social', 357),
('interested', 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),
('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),
('imagine', 321),
('kept', 320),
('office', 320),
('uses', 319),
('pure', 318),
('wait', 318),
('stunning', 318),
('review', 317),
('previous', 317),
('copy', 317),
('seriously', 317),
('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),
('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 [21]:

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 [22]:

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

``````
``````

Out[22]:

[('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),
('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),
('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),
('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),
('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),
('columbo', 0.82667857318446791),
('jake', 0.82667857318446791),
('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),
('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),
('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),
('china', 0.72489587887452556),
('anger', 0.72439972406404984),
('society', 0.72433010799663333),
('available', 0.72415741730250549),
('best', 0.72347034060446314),
('bugs', 0.72270598280148979),
('magic', 0.71878961117328299),
('delivers', 0.71846498854423513),
('verhoeven', 0.71846498854423513),
('jim', 0.71783979315031676),
('donald', 0.71667767797013937),
('endearing', 0.71465338578090898),
('relationships', 0.71393795022901896),
('greatly', 0.71256526641704687),
('charlie', 0.71024161391924534),
('simon', 0.70967648251115578),
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('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 [23]:

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

``````
``````

Out[23]:

[('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 [24]:

from IPython.display import Image

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

Image(filename='sentiment_network.png')

``````
``````

Out[24]:

``````
``````

In [25]:

review = "The movie was excellent"

Image(filename='sentiment_network_pos.png')

``````
``````

Out[25]:

``````

# Project 2: Creating the Input/Output Data

``````

In [26]:

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

``````
``````

74074

``````
``````

In [27]:

list(vocab)

``````
``````

Out[27]:

['',
'flaunted',
'wisecrack',
'dehavilland',
'futurism',
'actiona',
'obeisance',
'huuuuuuuarrrrghhhhhh',
'dirs',
'synecdoche',
'tvnz',
'candlelit',
'avati',
'cylinders',
'gorging',
'homelessness',
'unredemable',
'asian',
'dr',
'wish',
'grubbing',
'bonded',
'weepy',
'blackbuster',
'snipped',
'tenuous',
'epithet',
'iraquis',
'meself',
'dellacqua',
'remotest',
'cliquey',
'activates',
'obligated',
'empathizing',
'harchard',
'flourescent',
'tellers',
'lasers',
'desdemona',
'athanly',
'schmoozed',
'groovay',
'lp',
'zhu',
'securing',
'explitive',
'abating',
'cancelled',
'jerol',
'banishing',
'puszta',
'auds',
'estrogen',
'indebtedness',
'parroting',
'cullen',
'tremendously',
'panting',
'fickleness',
'nailed',
'brainwave',
'aquawhite',
'shootem',
'writhes',
'quoted',
'leoncavallo',
'magrath',
'happens',
'softener',
'cents',
'sharpville',
'genina',
'brooklyners',
'expertise',
'felicity',
'hrishita',
'preservatives',
'barfuss',
'fieriness',
'unasco',
'ditching',
'dimensionally',
'imperative',
'popistasu',
'delineated',
'infertile',
'medicinal',
'staryou',
'embarrass',
'sooooooooo',
'trance',
'hamnett',
'miming',
'shinning',
'resourcecenter',
'babble',
'liberators',
'boskovich',
'curiousness',
'commencement',
'peschi',
'inhaler',
'humble',
'scatting',
'avidly',
'dived',
'overstyling',
'geurilla',
'barbu',
'marched',
'cratchitt',
'maille',
'mollusks',
'kronfeld',
'maturely',
'embezzles',
'finley',
'stethoscope',
'satirically',
'du',
'pilliar',
'dagon',
'nominating',
'runs',
'bouncier',
'barril',
'frye',
'marija',
'lei',
'fatted',
'unannounced',
'crispin',
'duchovony',
'salliwan',
'gouald',
'noise',
'investments',
'boringly',
'flounces',
'bouncer',
'volo',
'circuited',
'surfaced',
'trinneer',
'muril',
'stimpy',
'panned',
'augers',
'jacknife',
'maos',
'warp',
'stall',
'messege',
'fide',
'mysteriousness',
'stanislavski',
'reportedly',
'exorbitant',
'aplogise',
'revival',
'bemoans',
'ultraboring',
'stis',
'mallow',
'saif',
'maxed',
'frilly',
'yesteryears',
'frankenstien',
'chalked',
'marti',
'greenhorn',
'brennecke',
'burly',
'yoke',
'corson',
'portentously',
'hawai',
'rasulala',
'or',
'blooming',
'ample',
'tributaries',
'caterpillar',
'luncheonette',
'insoluble',
'anbthony',
'weed',
'thereinafter',
'blight',
'dumbs',
'extreamly',
'heyday',
'gulliver',
'wok',
'ying',
'myth',
'steal',
'mcbride',
'carmack',
'yanking',
'togar',
'employers',
'barely',
'sybil',
'ler',
'temps',
'exclaim',
'homophobes',
'fierce',
'casablanca',
'coach',
'sloth',
'quincey',
'eyesore',
'apanowicz',
'poisoning',
'subdue',
'lookinland',
'bargepoles',
'jeunet',
'morricone',
'tua',
'mescaline',
'zdenek',
'eco',
'merendino',
'shines',
'larroz',
'qua',
'chhote',
'satanists',
'universality',
'theakston',
'caricias',
'underly',
'stamped',
'houselessness',
'unsatisfied',
'makeups',
'multifaceted',
'diahnn',
'packs',
'cute',
'zooming',
'nambi',
'also',
'introductions',
'bratislav',
'abashed',
'shoulda',
'coordinates',
'situation',
'judas',
'severally',
'miscarriages',
'thoughtfully',
'cologne',
'snooze',
'pandering',
'scalpels',
'ascending',
'help',
'consents',
'felecia',
'devlin',
'melora',
'westboro',
'tellytubbies',
'eduard',
'sheri',
'heiland',
'grue',
'outpouring',
'italianness',
'mercial',
'joburg',
'lookouts',
'pabst',
'rustam',
'metacinema',
'nook',
'columbus',
'letup',
'shelved',
'tote',
'silos',
'russkies',
'diggs',
'munnabhai',
'hypothesis',
'vermicelli',
'mukherjee',
'hutt',
'beauregard',
'shh',
'pock',
'immature',
'sneaky',
'suprematy',
'greevus',
'assimilation',
'waterman',
'furthermore',
'unhealthy',
'manone',
'zuthe',
'bassett',
'germann',
'kanmuri',
'auditor',
'splashing',
'tudor',
'foreshortened',
'hallan',
'problem',
'riders',
'sweetwater',
'superchick',
'cspan',
'amigos',
'incestuous',
'rotton',
'suman',
'houses',
'ipolite',
'gainey',
'revise',
'bayldon',
'muto',
'ancients',
'onj',
'rapping',
'sansabelt',
'semen',
'handelman',
'sunlit',
'cmdr',
'wyld',
'looming',
'nibelungenlied',
'francescoli',
'hammiest',
'vulkin',
'vaio',
'arbiter',
'rossi',
'rauol',
'defeating',
'muldaur',
'behaving',
'elixirs',
'dude',
'len',
'sorbet',
'dedlocks',
'willfully',
'perpetrated',
'lump',
'fantafestival',
'shopgirl',
'lunes',
'supplying',
'professionals',
'unenthusiastic',
'bakhtiari',
'compensating',
'sassier',
'yackin',
'hemlines',
'inaugurated',
'danayel',
'solondz',
'divisiveness',
'wans',
'denote',
'kanedaaa',
'georgia',
'clientle',
'concieling',
'fluidity',
'grating',
'incrediably',
'moaned',
'cardone',
'artifices',
'erase',
'hatton',
'alistar',
'basiclly',
'kangho',
'nestor',
'gaga',
'thesp',
'aproned',
'aborted',
'pirated',
'plotkurt',
'piere',
'write',
'tossed',
'circa',
'eardrum',
'softer',
'waning',
'murmuring',
'auburn',
'resisted',
'feebly',
'escriba',
'asiaphile',
'thoughtlessness',
'fasinating',
'auditory',
'distractions',
'formulate',
'shrinking',
'stevson',
'weclome',
'invierno',
'decoder',
'tnn',
'revelled',
'secure',
'eddington',
'scrambling',
'roeg',
'gourmet',
'spenser',
'segall',
'effortless',
'medicals',
'quentin',
'kramer',
'viewer',
'burgle',
'doorways',
'molotov',
'trappings',
'unwritten',
'flocker',
'garrick',
'kurita',
'fern',
'drippy',
'natsuyagi',
'maritally',
'rememberances',
'domesticate',
'comprehension',
'categorise',
'drenching',
'peakfire',
'overindulging',
'baptiste',
'vicotria',
'outr',
'sublimate',
'traction',
'osama',
'distinguishes',
'suckiest',
'jeong',
'phylicia',
'wasters',
'stroking',
'survive',
'fiscal',
'fat',
'surprised',
'widely',
'aggravated',
'overal',
'belfast',
'arose',
'arthouse',
'unmanipulated',
'annex',
'shimmying',
'oiled',
'therefore',
'bandido',
'blethyn',
'schmaltzy',
'villacheze',
'sacredness',
'mullins',
'facilitate',
'canby',
'preaching',
'cheetor',
'criterion',
'implementation',
'scrappys',
'cognates',
'wether',
'totin',
'pointjust',
'karr',
'dandelions',
'insisting',
'spiritually',
'gwoemul',
'hartley',
'avalon',
'treatise',
'jaime',
'inly',
'muller',
'pickups',
'halfway',
'brandi',
'charteris',
'shita',
'transplant',
'benshi',
'proposed',
'bibbidy',
'nvm',
'footloosing',
'ctv',
'glamourise',
'voiceless',
'wookie',
'dreamscape',
'daniella',
'older',
'stryker',
'karino',
'heaves',
'dan',
'hamstrung',
'rationale',
'mucked',
'undeservedly',
'wittgenstein',
'withbedlam',
'dobson',
'intergender',
'tibetian',
'redolent',
'attendance',
'airborne',
'helping',
'destructed',
'fame',
'regulations',
'offsets',
'kller',
'mayhem',
'opulently',
'sweey',
'burglarizing',
'rankles',
'ascerbic',
'curiousity',
'defray',
'offshoot',
'sharkish',
'basso',
'nietzsches',
'direstion',
'feinstone',
'foretold',
'yiannis',
'flockofducks',
'eloquently',
'fetishises',
'dodds',
'aiieeee',
'irvin',
'membership',
'filmgoers',
'favourably',
'witticism',
'shindler',
'inequality',
'killerher',
'pol',
'kamp',
'stanislavsky',
'psychotics',
'enviably',
'quotable',
'commemorated',
'nominee',
'certified',
'onwhich',
'birthparents',
'orla',
'agekudos',
'cusamanos',
'tastic',
'pillman',
'devoting',
'viver',
'littlekuriboh',
'concubine',
'dumbsh',
'jogging',
'signature',
'orphee',
'hamiltons',
'cheat',
'violator',
'traffickers',
'corkymeter',
'panels',
'misunderstood',
'captured',
'ashlee',
'sellick',
'peeked',
'cukor',
'sleepwalking',
'macmurray',
'retraces',
'halluzinations',
'caucasian',
'mr',
'frail',
'fags',
'knightrider',
'talmudic',
'exercising',
'beacon',
'peppard',
'brazil',
'levitt',
'intercedes',
'deduced',
'fogie',
'powwow',
'commericals',
'elsewhere',
'dogie',
'maniac',
'mommas',
'atleast',
'patronage',
'kendrick',
'sex',
'substitution',
'seoul',
'sissy',
'sandrich',
'enhancement',
'drinker',
'fusanosuke',
'okinawa',
'grod',
'napkin',
'marginalization',
'seizing',
'konerak',
'swastikas',
'begged',
'cothk',
'mchael',
'sequelae',
'whirlwind',
'anywaythis',
'scroll',
'comandante',
'kikuno',
'ngo',
'traumatized',
'ukrainian',
'greystone',
'mindy',
'weakened',
'fetched',
'strict',
'streaking',
'kellaway',
'truffle',
'ffwd',
'michener',
'achieved',
'tux',
'malte',
'tisserand',
'hurdle',
'engineering',
'victor',
'spends',
'sokurov',
'ages',
'mclaglan',
'monstrously',
'peg',
'rulezzz',
'eschatalogy',
'thinked',
'narayan',
'canon',
'vonda',
'mooner',
'swallow',
'minefield',
'brooksophile',
'wishbone',
'somers',
'laureen',
'rve',
'hyper',
'koshiro',
'muffin',
'bodily',
'typo',
'vestiges',
'frontline',
'finito',
'untouchables',
'cooks',
'karyn',
'effects',
'willpower',
'balcony',
'ponderance',
'parlor',
'heartedness',
'abdul',
'terrorizing',
'kirkendalls',
'tbs',
'artset',
'aaaaaaah',
'carlottai',
'focussing',
'flashes',
'flaccid',
'bernstein',
'crasser',
'fantasists',
'sorte',
'aquaintance',
'ceeb',
'lolita',
'caldwell',
'lime',
'gayer',
'lit',
'locken',
'wowser',
'mutate',
'slicker',
'griswolds',
'classed',
'cutters',
'showered',
'piece',
'actually',
'actores',
'leesa',
'undue',
'shagging',
'urbane',
'chillness',
'heidelberg',
'jannings',
'debriefed',
'sematically',
'article',
'lanford',
'gambit',
'gain',
'eowyn',
'asther',
'eachother',
'litvak',
'berner',
'kaneko',
'mobarak',
'likeminded',
'gossip',
'donger',
'pathe',
'traitor',
'sacha',
'kucch',
'ruse',
'hoodwink',
'subsidize',
'sais',
'eurpeans',
'bffs',
'coop',
'tolerant',
'chubby',
'withhold',
'unseated',
'odessy',
'powers',
'vomitum',
'nyc',
'hounfor',
'hankers',
'swear',
'ransacking',
'pertinent',
'pensaba',
'plank',
'wedge',
'ocar',
'disconcerting',
'accord',
'reinvention',
'abortions',
'dispenses',
'bogarde',
'engletine',
'vatican',
'officers',
'enduring',
'entourage',
'tibbett',
'shavian',
'zomcon',
'divergences',
'condo',
'prominent',
'unprecedented',
'coroner',
'ortolani',
'lately',
'douche',
'dusting',
'condescends',
'soupon',
'incantations',
'symbolizing',
'vanquishing',
'grants',
'velankar',
'heeding',
'ticking',
'wray',
'glue',
'ringer',
'edouard',
'theirs',
'eliana',
'canoes',
'bosnia',
'skycaptain',
'wertmuller',
'etebari',
'unimaginable',
'yrs',
'disproving',
'tcheky',
'monsieur',
'minced',
'docs',
'sinisterness',
'suucks',
'pueblo',
'khan',
'disarm',
'politeness',
'jive',
'carted',
'reptilian',
'rakowsky',
'implausability',
'protester',
'ppv',
'stomaches',
'okej',
'schappert',
'fortress',
'teresa',
'incumbent',
'viccaro',
'doodle',
'crossover',
'lancaster',
'malaysian',
'blooper',
'tearjerking',
'simonsons',
'epileptic',
'cots',
'protagonist',
'liberty',
'pollinating',
'siska',
'surrogacy',
'constrict',
'belivable',
'cosy',
'springer',
'nonintentional',
'torturing',
'dyeing',
'ckco',
'lonelygirl',
'berkhoff',
'flubber',
'druidic',
'luxuriant',
'styrofoam',
'forges',
'hhoorriibbllee',
'audaciously',
'overdub',
'comprehend',
'conciseness',
'cameroons',
'clumsiness',
'dostoyevky',
'mules',
'swoosie',
'hustling',
'schtock',
'ghajini',
'removes',
'leg',
'overtaking',
'overburdening',
'rhyes',
'mown',
'comely',
'drier',
'jumpy',
'activated',
'inhuman',
'wozzeck',
'amnesty',
'trattoria',
'delirium',
'daughter',
'giddily',
'believes',
'sultan',
'omniscient',
'dem',
'tomboy',
'fumble',
'impressionists',
'expanses',
'tannen',
'horace',
'thomersons',
'vowel',
'preys',
'mountainbillies',
'razor',
'calamai',
'travelled',
'neverending',
'izes',
'reexamined',
'moviemakers',
'donahue',
'gemser',
'mistrust',
'ideologue',
'daysthis',
'thankful',
'disability',
'roach',
't',
'zooms',
'convenience',
'elevation',
'molt',
'suspects',
'stylised',
'absorbed',
'sawpart',
'flunks',
...]

``````
``````

In [28]:

import numpy as np

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

``````
``````

Out[28]:

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

``````
``````

In [29]:

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

``````
``````

Out[29]:

``````
``````

In [30]:

word2index = {}

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

``````
``````

Out[30]:

{'': 0,
'flaunted': 1,
'wisecrack': 2,
'dehavilland': 3,
'futurism': 4,
'actiona': 6,
'obeisance': 7,
'huuuuuuuarrrrghhhhhh': 8,
'dirs': 9,
'synecdoche': 10,
'tvnz': 11,
'candlelit': 12,
'avati': 13,
'cylinders': 14,
'gorging': 15,
'homelessness': 16,
'unredemable': 17,
'asian': 18,
'dr': 19,
'wish': 20,
'grubbing': 21,
'bonded': 22,
'weepy': 23,
'blackbuster': 24,
'snipped': 25,
'tenuous': 26,
'epithet': 27,
'iraquis': 28,
'meself': 29,
'dellacqua': 30,
'remotest': 31,
'cliquey': 32,
'activates': 33,
'obligated': 34,
'empathizing': 35,
'harchard': 36,
'flourescent': 37,
'tellers': 38,
'lasers': 39,
'desdemona': 40,
'athanly': 41,
'schmoozed': 42,
'groovay': 43,
'lp': 44,
'zhu': 45,
'securing': 46,
'explitive': 47,
'abating': 48,
'cancelled': 49,
'jerol': 50,
'banishing': 51,
'puszta': 52,
'auds': 53,
'estrogen': 54,
'indebtedness': 55,
'parroting': 56,
'cullen': 57,
'tremendously': 58,
'panting': 59,
'fickleness': 60,
'nailed': 61,
'brainwave': 62,
'aquawhite': 63,
'shootem': 64,
'writhes': 65,
'quoted': 66,
'leoncavallo': 67,
'magrath': 68,
'happens': 69,
'softener': 70,
'cents': 72,
'sharpville': 73,
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'brooklyners': 75,
'expertise': 76,
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'hrishita': 78,
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'medicinal': 90,
'staryou': 91,
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'sooooooooo': 93,
'trance': 94,
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'miming': 96,
'shinning': 97,
'resourcecenter': 98,
'babble': 99,
'liberators': 100,
'boskovich': 101,
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'commencement': 103,
'peschi': 104,
'inhaler': 105,
'humble': 106,
'scatting': 107,
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'overstyling': 111,
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'barbu': 113,
'marched': 114,
'cratchitt': 115,
'maille': 116,
'mollusks': 117,
'kronfeld': 118,
'maturely': 119,
'embezzles': 120,
'finley': 121,
'stethoscope': 122,
'satirically': 123,
'du': 124,
'pilliar': 125,
'dagon': 126,
'nominating': 127,
'runs': 128,
'bouncier': 129,
'barril': 130,
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'marija': 132,
'lei': 133,
'fatted': 134,
'unannounced': 135,
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'duchovony': 137,
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'diahnn': 249,
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'shoulda': 259,
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'miscarriages': 264,
'thoughtfully': 265,
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'melora': 275,
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'tudor': 321,
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'moaned': 392,
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'hartley': 515,
'avalon': 516,
'treatise': 517,
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'muller': 520,
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'shita': 525,
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'proposed': 528,
'bibbidy': 529,
'nvm': 531,
'footloosing': 532,
'ctv': 533,
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'wookie': 536,
'dreamscape': 537,
'daniella': 538,
'older': 539,
'stryker': 540,
'karino': 541,
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'dan': 543,
'hamstrung': 544,
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'mucked': 546,
'undeservedly': 547,
'wittgenstein': 548,
'withbedlam': 549,
'dobson': 550,
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'tibetian': 552,
'redolent': 553,
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'membership': 584,
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'killerher': 591,
'pol': 592,
'kamp': 593,
'stanislavsky': 594,
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'enviably': 596,
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'certified': 600,
'onwhich': 601,
'birthparents': 602,
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'agekudos': 604,
'cusamanos': 605,
'tastic': 606,
'pillman': 607,
'devoting': 608,
'viver': 609,
'littlekuriboh': 610,
'concubine': 611,
'dumbsh': 612,
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'signature': 614,
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'traffickers': 619,
'corkymeter': 620,
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'ashlee': 624,
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'mr': 633,
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'fogie': 645,
'powwow': 646,
'commericals': 648,
'elsewhere': 649,
'dogie': 650,
'maniac': 651,
'mommas': 652,
'atleast': 653,
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'sandrich': 660,
'enhancement': 661,
'drinker': 662,
'fusanosuke': 663,
'okinawa': 664,
'grod': 665,
'napkin': 666,
'marginalization': 667,
'seizing': 668,
'konerak': 669,
'swastikas': 670,
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'mchael': 673,
'sequelae': 674,
'whirlwind': 675,
'anywaythis': 676,
'scroll': 677,
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'ngo': 680,
'traumatized': 681,
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'mindy': 684,
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'strict': 687,
'streaking': 688,
'kellaway': 689,
'truffle': 690,
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'michener': 692,
'achieved': 693,
'tux': 694,
'malte': 695,
'tisserand': 696,
'hurdle': 697,
'engineering': 698,
'victor': 699,
'spends': 701,
'sokurov': 702,
'ages': 703,
'mclaglan': 704,
'monstrously': 705,
'peg': 706,
'rulezzz': 707,
'eschatalogy': 708,
'thinked': 709,
'narayan': 710,
'canon': 711,
'vonda': 712,
'mooner': 713,
'swallow': 714,
'minefield': 715,
'brooksophile': 716,
'wishbone': 717,
'somers': 718,
'laureen': 719,
'rve': 720,
'hyper': 721,
'koshiro': 722,
'muffin': 723,
'bodily': 724,
'typo': 725,
'vestiges': 726,
'frontline': 727,
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'cooks': 730,
'karyn': 731,
'effects': 732,
'willpower': 733,
'balcony': 734,
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'terrorizing': 739,
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'tbs': 741,
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'fantasists': 750,
'sorte': 751,
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'mutate': 762,
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'cutters': 766,
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'urbane': 774,
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'heidelberg': 777,
'jannings': 779,
'debriefed': 780,
'sematically': 781,
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'eowyn': 786,
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'eachother': 788,
'litvak': 789,
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'kaneko': 791,
'mobarak': 792,
'likeminded': 793,
'gossip': 794,
'donger': 795,
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'sacha': 800,
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'hoodwink': 803,
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'lonelygirl': 921,
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'cameroons': 933,
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'mules': 936,
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'removes': 941,
'leg': 942,
'overtaking': 943,
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'rhyes': 945,
'mown': 946,
'comely': 947,
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'jumpy': 949,
'activated': 950,
'inhuman': 951,
'wozzeck': 952,
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'trattoria': 954,
'delirium': 955,
'daughter': 956,
'giddily': 957,
'believes': 958,
'sultan': 959,
'omniscient': 960,
'dem': 961,
'tomboy': 962,
'fumble': 964,
'impressionists': 965,
'expanses': 966,
'tannen': 967,
'horace': 968,
'thomersons': 969,
'vowel': 970,
'preys': 971,
'mountainbillies': 972,
'razor': 973,
'calamai': 974,
'travelled': 975,
'neverending': 976,
'izes': 977,
'reexamined': 978,
'moviemakers': 979,
'donahue': 980,
'gemser': 981,
'mistrust': 982,
'ideologue': 983,
'daysthis': 984,
'thankful': 985,
'disability': 986,
'roach': 987,
't': 988,
'zooms': 989,
'convenience': 990,
'elevation': 991,
'molt': 992,
'suspects': 993,
'stylised': 994,
'absorbed': 996,
'sawpart': 997,
'flunks': 998,
...}

``````
``````

In [31]:

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 [32]:

layer_0

``````
``````

Out[32]:

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

``````
``````

In [33]:

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

``````
``````

In [34]:

labels[0]

``````
``````

Out[34]:

'POSITIVE'

``````
``````

In [35]:

get_target_for_label(labels[0])

``````
``````

Out[35]:

1

``````
``````

In [36]:

labels[1]

``````
``````

Out[36]:

'NEGATIVE'

``````
``````

In [37]:

get_target_for_label(labels[1])

``````
``````

Out[37]:

0

``````

# Project 3: Building a Neural Network

• 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 [38]:

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(" "):
self.review_vocab = list(review_vocab)

label_vocab = set()
for label in labels:

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 [39]:

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

``````
``````

In [40]:

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

``````
``````

Progress:99.9% Speed(reviews/sec):480.6% #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)
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)
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)
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 [67]:

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

``````
``````

Out[67]:

``````
``````

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),
('pity', 1),
('m', 1),
('all', 1),
('see', 1),
('think', 1),
('situation', 1),
('time', 1),
('pomp', 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 [82]:

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(" "):
self.review_vocab = list(review_vocab)

label_vocab = set()
for label in labels:

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 [25]:

Image(filename='sentiment_network_sparse.png')

``````
``````

Out[25]:

``````
``````

In [26]:

layer_0 = np.zeros(10)

``````
``````

In [27]:

layer_0

``````
``````

Out[27]:

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

``````
``````

In [28]:

layer_0[4] = 1
layer_0[9] = 1

``````
``````

In [29]:

layer_0

``````
``````

Out[29]:

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

``````
``````

In [30]:

weights_0_1 = np.random.randn(10,5)

``````
``````

In [31]:

layer_0.dot(weights_0_1)

``````
``````

Out[31]:

array([-0.89068523, -1.00872504, -2.92095519,  0.85671495, -0.69113514])

``````
``````

In [32]:

indices = [4,9]

``````
``````

In [33]:

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 [100]:

Image(filename='sentiment_network_sparse_2.png')

``````
``````

Out[100]:

``````

# Project 5: Making our Network More Efficient

``````

In [34]:

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(" "):
self.review_vocab = list(review_vocab)

label_vocab = set()
for label in labels:

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()):
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():
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 [106]:

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

``````
``````

In [111]:

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

``````
``````

In [109]:

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

``````
``````

Progress:99.9% Speed(reviews/sec):1581.% #Correct:857 #Tested:1000 Testing Accuracy:85.7%

``````

# Further Noise Reduction

``````

In [41]:

Image(filename='sentiment_network_sparse_2.png')

``````
``````

Out[41]:

``````
``````

In [42]:

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

``````
``````

Out[42]:

[('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),
('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),
('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),
('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),
('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),
('columbo', 0.82667857318446791),
('jake', 0.82667857318446791),
('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),
('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),
('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),
('china', 0.72489587887452556),
('anger', 0.72439972406404984),
('society', 0.72433010799663333),
('available', 0.72415741730250549),
('best', 0.72347034060446314),
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('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 [43]:

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

``````
``````

Out[43]:

[('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 [44]:

from bokeh.models import ColumnDataSource, LabelSet
from bokeh.plotting import figure, show, output_file
from bokeh.io import output_notebook
output_notebook()

``````
``````

var element = \$('#b2dd893a-cdff-4dc6-992e-8d08f7d92447');

(function(global) {
function now() {
return new Date();
}

var force = true;

if (typeof (window._bokeh_onload_callbacks) === "undefined" || force === true) {
}

if (typeof (window._bokeh_timeout) === "undefined" || force === true) {
window._bokeh_timeout = Date.now() + 5000;
}

"<div style='background-color: #fdd'>\n"+
"<p>\n"+
"may be due to a slow or bad network connection. Possible fixes:\n"+
"</p>\n"+
"<ul>\n"+
"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\n"+
"<li>use INLINE resources instead, as so:</li>\n"+
"</ul>\n"+
"<code>\n"+
"from bokeh.resources import INLINE\n"+
"output_notebook(resources=INLINE)\n"+
"</code>\n"+
"</div>"}};

if (window.Bokeh !== undefined) {
var el = document.getElementById("4568a863-51eb-4aca-b509-e3ff9760fc90");
el.textContent = "BokehJS " + Bokeh.version + " successfully loaded.";
} else if (Date.now() < window._bokeh_timeout) {
}
}

function run_callbacks() {
console.info("Bokeh: all callbacks have finished");
}

console.log("Bokeh: BokehJS is being loaded, scheduling callback at", now());
return null;
}
if (js_urls == null || js_urls.length === 0) {
run_callbacks();
return null;
}
for (var i = 0; i < js_urls.length; i++) {
var url = js_urls[i];
var s = document.createElement('script');
s.src = url;
s.async = false;
run_callbacks()
}
};
s.onerror = function() {
console.warn("failed to load library " + url);
};
console.log("Bokeh: injecting script tag for BokehJS library: ", url);
}
};var element = document.getElementById("4568a863-51eb-4aca-b509-e3ff9760fc90");
if (element == null) {
console.log("Bokeh: ERROR: autoload.js configured with elementid '4568a863-51eb-4aca-b509-e3ff9760fc90' but no matching script tag was found. ")
return false;
}

var js_urls = ["https://cdn.pydata.org/bokeh/release/bokeh-0.12.5.min.js", "https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.5.min.js"];

var inline_js = [
function(Bokeh) {
Bokeh.set_log_level("info");
},

function(Bokeh) {

},

function(Bokeh) {

},
function(Bokeh) {
console.log("Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.5.min.css");
Bokeh.embed.inject_css("https://cdn.pydata.org/bokeh/release/bokeh-0.12.5.min.css");
console.log("Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.5.min.css");
Bokeh.embed.inject_css("https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.5.min.css");
}
];

function run_inline_js() {

if ((window.Bokeh !== undefined) || (force === true)) {
for (var i = 0; i < inline_js.length; i++) {
inline_js[i](window.Bokeh);
}if (force === true) {
}} else if (Date.now() < window._bokeh_timeout) {
setTimeout(run_inline_js, 100);
console.log("Bokeh: BokehJS failed to load within specified timeout.");
} else if (force !== true) {
var cell = \$(document.getElementById("4568a863-51eb-4aca-b509-e3ff9760fc90")).parents('.cell').data().cell;
}

}

console.log("Bokeh: BokehJS loaded, going straight to plotting");
run_inline_js();
} else {
console.log("Bokeh: BokehJS plotting callback run at", now());
run_inline_js();
});
}
}(this));

``````
``````

In [45]:

hist, edges = np.histogram(list(map(lambda x:x[1],pos_neg_ratios.most_common())), density=True, bins=100, normed=True)

p = figure(tools="pan,wheel_zoom,reset,save",
toolbar_location="above",
title="Word Positive/Negative Affinity Distribution")
show(p)

``````
``````

(function(global) {
function now() {
return new Date();
}

var force = false;

if (typeof (window._bokeh_onload_callbacks) === "undefined" || force === true) {
}

if (typeof (window._bokeh_timeout) === "undefined" || force === true) {
window._bokeh_timeout = Date.now() + 0;
}

"<div style='background-color: #fdd'>\n"+
"<p>\n"+
"may be due to a slow or bad network connection. Possible fixes:\n"+
"</p>\n"+
"<ul>\n"+
"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\n"+
"<li>use INLINE resources instead, as so:</li>\n"+
"</ul>\n"+
"<code>\n"+
"from bokeh.resources import INLINE\n"+
"output_notebook(resources=INLINE)\n"+
"</code>\n"+
"</div>"}};

if (window.Bokeh !== undefined) {
var el = document.getElementById("aebf96ac-e9dc-494d-a800-954f7b94cd04");
el.textContent = "BokehJS " + Bokeh.version + " successfully loaded.";
} else if (Date.now() < window._bokeh_timeout) {
}
}

function run_callbacks() {
console.info("Bokeh: all callbacks have finished");
}

console.log("Bokeh: BokehJS is being loaded, scheduling callback at", now());
return null;
}
if (js_urls == null || js_urls.length === 0) {
run_callbacks();
return null;
}
for (var i = 0; i < js_urls.length; i++) {
var url = js_urls[i];
var s = document.createElement('script');
s.src = url;
s.async = false;
run_callbacks()
}
};
s.onerror = function() {
console.warn("failed to load library " + url);
};
console.log("Bokeh: injecting script tag for BokehJS library: ", url);
}
};var element = document.getElementById("aebf96ac-e9dc-494d-a800-954f7b94cd04");
if (element == null) {
console.log("Bokeh: ERROR: autoload.js configured with elementid 'aebf96ac-e9dc-494d-a800-954f7b94cd04' but no matching script tag was found. ")
return false;
}

var js_urls = [];

var inline_js = [
function(Bokeh) {
(function() {
var fn = function() {
var render_items = [{"docid":"7bebd591-6392-4cc3-b82a-b9eaab8e783c","elementid":"aebf96ac-e9dc-494d-a800-954f7b94cd04","modelid":"082a1e41-5fa0-4315-886e-0d1462c63ea3"}];

Bokeh.embed.embed_items(docs_json, render_items);
};
})();
},
function(Bokeh) {
}
];

function run_inline_js() {

if ((window.Bokeh !== undefined) || (force === true)) {
for (var i = 0; i < inline_js.length; i++) {
inline_js[i](window.Bokeh);
}if (force === true) {
}} else if (Date.now() < window._bokeh_timeout) {
setTimeout(run_inline_js, 100);
console.log("Bokeh: BokehJS failed to load within specified timeout.");
} else if (force !== true) {
var cell = \$(document.getElementById("aebf96ac-e9dc-494d-a800-954f7b94cd04")).parents('.cell').data().cell;
}

}

console.log("Bokeh: BokehJS loaded, going straight to plotting");
run_inline_js();
} else {
console.log("Bokeh: BokehJS plotting callback run at", now());
run_inline_js();
});
}
}(this));

``````
``````

In [46]:

frequency_frequency = Counter()

for word, cnt in total_counts.most_common():
frequency_frequency[cnt] += 1

``````
``````

In [47]:

hist, edges = np.histogram(list(map(lambda x:x[1],frequency_frequency.most_common())), density=True, bins=100, normed=True)

p = figure(tools="pan,wheel_zoom,reset,save",
toolbar_location="above",
title="The frequency distribution of the words in our corpus")
show(p)

``````
``````

(function(global) {
function now() {
return new Date();
}

var force = false;

if (typeof (window._bokeh_onload_callbacks) === "undefined" || force === true) {
}

if (typeof (window._bokeh_timeout) === "undefined" || force === true) {
window._bokeh_timeout = Date.now() + 0;
}

"<div style='background-color: #fdd'>\n"+
"<p>\n"+
"may be due to a slow or bad network connection. Possible fixes:\n"+
"</p>\n"+
"<ul>\n"+
"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\n"+
"<li>use INLINE resources instead, as so:</li>\n"+
"</ul>\n"+
"<code>\n"+
"from bokeh.resources import INLINE\n"+
"output_notebook(resources=INLINE)\n"+
"</code>\n"+
"</div>"}};

if (window.Bokeh !== undefined) {
var el = document.getElementById("01ffe80e-6b43-4e68-bef5-a76d9d66a8e5");
el.textContent = "BokehJS " + Bokeh.version + " successfully loaded.";
} else if (Date.now() < window._bokeh_timeout) {
}
}

function run_callbacks() {
console.info("Bokeh: all callbacks have finished");
}

console.log("Bokeh: BokehJS is being loaded, scheduling callback at", now());
return null;
}
if (js_urls == null || js_urls.length === 0) {
run_callbacks();
return null;
}
for (var i = 0; i < js_urls.length; i++) {
var url = js_urls[i];
var s = document.createElement('script');
s.src = url;
s.async = false;
run_callbacks()
}
};
s.onerror = function() {
console.warn("failed to load library " + url);
};
console.log("Bokeh: injecting script tag for BokehJS library: ", url);
}
};var element = document.getElementById("01ffe80e-6b43-4e68-bef5-a76d9d66a8e5");
if (element == null) {
console.log("Bokeh: ERROR: autoload.js configured with elementid '01ffe80e-6b43-4e68-bef5-a76d9d66a8e5' but no matching script tag was found. ")
return false;
}

var js_urls = [];

var inline_js = [
function(Bokeh) {
(function() {
var fn = function() {

Bokeh.embed.embed_items(docs_json, render_items);
};
})();
},
function(Bokeh) {
}
];

function run_inline_js() {

if ((window.Bokeh !== undefined) || (force === true)) {
for (var i = 0; i < inline_js.length; i++) {
inline_js[i](window.Bokeh);
}if (force === true) {
}} else if (Date.now() < window._bokeh_timeout) {
setTimeout(run_inline_js, 100);
console.log("Bokeh: BokehJS failed to load within specified timeout.");
} else if (force !== true) {
var cell = \$(document.getElementById("01ffe80e-6b43-4e68-bef5-a76d9d66a8e5")).parents('.cell').data().cell;
}

}

console.log("Bokeh: BokehJS loaded, going straight to plotting");
run_inline_js();
} else {
console.log("Bokeh: BokehJS plotting callback run at", now());
run_inline_js();
});
}
}(this));

``````

# Reducing Noise by Strategically Reducing the Vocabulary

``````

In [48]:

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,min_count = 10,polarity_cutoff = 0.1,hidden_nodes = 10, learning_rate = 0.1):

np.random.seed(1)

self.pre_process_data(reviews, polarity_cutoff, min_count)

self.init_network(len(self.review_vocab),hidden_nodes, 1, learning_rate)

def pre_process_data(self,reviews, polarity_cutoff,min_count):

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

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

pos_neg_ratios = Counter()

for term,cnt in list(total_counts.most_common()):
if(cnt >= 50):
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)))

review_vocab = set()
for review in reviews:
for word in review.split(" "):
if(total_counts[word] > min_count):
if(word in pos_neg_ratios.keys()):
if((pos_neg_ratios[word] >= polarity_cutoff) or (pos_neg_ratios[word] <= -polarity_cutoff)):
else:
self.review_vocab = list(review_vocab)

label_vocab = set()
for label in labels:

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()):
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(layer_2 >= 0.5 and label == 'POSITIVE'):
correct_so_far += 1
if(layer_2 < 0.5 and label == 'NEGATIVE'):
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():
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 [49]:

mlp = SentimentNetwork(reviews[:-1000],labels[:-1000],min_count=20,polarity_cutoff=0.05,learning_rate=0.01)

``````
``````

In [50]:

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

``````
``````

Progress:99.9% Speed(reviews/sec):827.2 #Correct:20461 #Trained:24000 Training Accuracy:85.2%

``````
``````

In [51]:

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

``````
``````

Progress:99.9% Speed(reviews/sec):1329.% #Correct:859 #Tested:1000 Testing Accuracy:85.9%

``````
``````

In [52]:

mlp = SentimentNetwork(reviews[:-1000],labels[:-1000],min_count=20,polarity_cutoff=0.8,learning_rate=0.01)

``````
``````

In [53]:

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

``````
``````

Progress:99.9% Speed(reviews/sec):2798. #Correct:20552 #Trained:24000 Training Accuracy:85.6%

``````
``````

In [54]:

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

``````
``````

Progress:99.9% Speed(reviews/sec):2396.% #Correct:822 #Tested:1000 Testing Accuracy:82.2%

``````
``````

In [ ]:

``````