# 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 [1]:

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 [2]:

len(reviews)

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

Out[2]:

25000

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

In [3]:

reviews[0]

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

Out[3]:

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

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

In [4]:

labels[0]

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

Out[4]:

'POSITIVE'

``````

# Lesson: Develop a Predictive Theory

``````

In [5]:

print("labels.txt \t : \t reviews.txt\n")
pretty_print_review_and_label(2137)
pretty_print_review_and_label(12816)
pretty_print_review_and_label(6267)
pretty_print_review_and_label(21934)
pretty_print_review_and_label(5297)
pretty_print_review_and_label(4998)

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

labels.txt 	 : 	 reviews.txt

NEGATIVE	:	this movie is terrible but it has some good effects .  ...
POSITIVE	:	adrian pasdar is excellent is this film . he makes a fascinating woman .  ...
NEGATIVE	:	comment this movie is impossible . is terrible  very improbable  bad interpretat...
POSITIVE	:	excellent episode movie ala pulp fiction .  days   suicides . it doesnt get more...
NEGATIVE	:	if you haven  t seen this  it  s terrible . it is pure trash . i saw this about ...
POSITIVE	:	this schiffer guy is a real genius  the movie is of excellent quality and both e...

``````

# Project 1: Quick Theory Validation

``````

In [9]:

from collections import Counter
import numpy as np

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

In [10]:

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

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

In [11]:

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 [12]:

positive_counts.most_common()

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

Out[12]:

[('', 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 [13]:

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 [14]:

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

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

Out[14]:

[('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 [15]:

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

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

Out[15]:

[('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 [16]:

from IPython.display import Image

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

Image(filename='sentiment_network.png')

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

Out[16]:

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

In [27]:

review = "The movie was excellent"

Image(filename='sentiment_network_pos.png')

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

Out[27]:

``````

# Project 2: Creating the Input/Output Data

``````

In [17]:

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

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

74074

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

In [18]:

list(vocab)

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

Out[18]:

['',
'touissant',
'muncey',
'fullscreen',
'manifesting',
'overplaying',
'tents',
'landscapes',
'silicone',
'lalanne',
'unobserved',
'godly',
'jianxiang',
'nachoo',
'acoustics',
'phillimines',
'pummeled',
'ejaculation',
'shugoro',
'substitute',
'turtles',
'shoddily',
'polanski',
'syafie',
'definative',
'gauteng',
'nonstop',
'buccaneering',
'weary',
'kji',
'arrrgghhh',
'agamemnon',
'seaward',
'triste',
'mortimer',
'indict',
'corruption',
'conjunction',
'cineastes',
'hirjee',
'profiles',
'channelling',
'kayak',
'gv',
'hershman',
'decisionsin',
'hemmingway',
'synch',
'walt',
'nob',
'cimarron',
'heisler',
'huxtables',
'juscar',
'funt',
'deterrent',
'scarynot',
'differentiation',
'rewatchability',
'vouch',
'dardino',
'clerking',
'matheisen',
'expeditiously',
'kohara',
'intros',
'dunn',
'construe',
'peacefulness',
'charter',
'bang',
'sergi',
'physically',
'gingernuts',
'definently',
'brasseur',
'ivay',
'ranged',
'reaming',
'leapt',
'shaft',
'segueing',
'hurting',
'goose',
'bonde',
'culled',
'scribbles',
'loris',
'purrrrrrrrrrrrrrrr',
'cameras',
'mischievous',
'gratitude',
'indebtedness',
'submarines',
'glamor',
'appease',
'romanticism',
'hll',
'louda',
'foodstuffs',
'preyer',
'pentameter',
'sourly',
'takers',
'hypersensitive',
'dismantle',
'dza',
'mou',
'snails',
'steely',
'effected',
'feather',
'strangest',
'woodlands',
'ingratiate',
'okey',
'sandbag',
'clowned',
'blackploitation',
'chaptered',
'macbeth',
'verve',
'mbongeni',
'malte',
'fomenting',
'bruhls',
'larded',
'vfcc',
'deewana',
'guptil',
'larp',
'demanding',
'resembled',
'extraterrestrial',
'harni',
'lynched',
'jbl',
'alleging',
'champmathieu',
'determination',
'patron',
'pertain',
'unimpressively',
'limousines',
'heterogeneity',
'hiller',
'snippers',
'tc',
'puppetry',
'nordham',
'detritus',
'frogballs',
'gourds',
'pagemaster',
'coveys',
'lucian',
'fragrant',
'afflicted',
'mahnaz',
'airline',
'sega',
'glower',
'musican',
'kudisch',
'snarls',
'theonly',
'mindbender',
'ebersole',
'crummiest',
'doorpost',
'brochures',
'gioconda',
'maine',
'mandelbaum',
'alexandria',
'afortunately',
'ungallant',
'debilitating',
'troupe',
'lactating',
'constanly',
'revere',
'inarticulate',
'neither',
'shimmer',
'metcalfe',
'casanova',
'umilak',
'vfx',
'terrorize',
'latches',
'machinist',
'deceived',
'vntoarea',
'cleaning',
'unchallenged',
'fiefdoms',
'percent',
'schoolroom',
'microscopically',
'li',
'faw',
'planning',
'bodysuit',
'transition',
'crumpled',
'sagr',
'deteriorated',
'goykiba',
'dynasty',
'steered',
'orchestration',
'redblock',
'wannabe',
'moretti',
'gring',
'jayston',
'natali',
'ayatollahs',
'candians',
'allan',
'reabsorbed',
'equipe',
'aberystwyth',
'chaparones',
'financiers',
'saws',
'sayuri',
'pip',
'duilio',
'significantly',
'norwegia',
'colonel',
'winged',
'netherworld',
'myspace',
'autopsied',
'courtesy',
'mouths',
'luxemburg',
'malevolence',
'slinging',
'bilardo',
'loveliness',
'shaping',
'bolha',
'mysteriosity',
'households',
'maximimum',
'sssr',
'aleck',
'biroc',
'airfield',
'coral',
'coped',
'distilled',
'talks',
'nineteenth',
'kennedys',
'pointblank',
'cleverness',
'wrinkle',
'ninga',
'doen',
'ramya',
'fastforwarding',
'blackguard',
'orry',
'gravitas',
'disastrously',
'snuff',
'schiff',
'aimlessness',
'paton',
'boars',
'retaining',
'tinting',
'insult',
'despondently',
'cindy',
'bobbies',
'diffident',
'super',
'eloquent',
'anchored',
'refuting',
'chastise',
'blatantly',
'narrate',
'leveling',
'caballo',
'weightwatchers',
'excruciatingly',
'whitt',
'concerning',
'kramer',
'farrakhan',
'sportsman',
'where',
'hauptmann',
'stationmaster',
'ugghh',
'swamps',
'hologram',
'whack',
'riffraff',
'withstood',
'confesses',
'weeds',
'discoverer',
'dewet',
'scrivener',
'embezzled',
'homepages',
'finding',
'compliance',
'frocked',
'unlockables',
'emek',
'latrines',
'detectives',
'elongate',
'ivanova',
'images',
'absolutly',
'southerrners',
'maaan',
'perceptible',
'chit',
'mccathy',
'gouden',
'harrods',
'obligatory',
'worth',
'counters',
'firms',
'disentangling',
'ryecart',
'analyze',
'caalling',
'platitudes',
'wow',
'cynthia',
'les',
'isolationist',
'drawback',
'wormwood',
'preens',
'rebelled',
'reviews',
'miser',
'burglary',
'farnel',
'groupthink',
'revivalist',
'snips',
'ruffin',
'dramatisations',
'ascendant',
'distraction',
'herapheri',
'neous',
'lampio',
'sophomore',
'gangfights',
'ingredients',
'afv',
'flavored',
'cauldrons',
'philosophy',
'wgbh',
'haje',
'thriteen',
'birkina',
'strangulations',
'neagle',
'precipice',
'syllable',
'countermeasures',
'punctures',
'holt',
'ter',
'rivire',
'mordantly',
'operas',
'insurgents',
'stepdaughter',
'vibrators',
'lepus',
'mmhm',
'doodle',
'bookcase',
'recreate',
'jorge',
'mandatory',
'agendas',
'reception',
'plexiglas',
'bubba',
'gender',
'jules',
'werecat',
'overdressed',
'foremost',
'eggbeater',
'horrify',
'tailored',
'cleaners',
'lansbury',
'alfie',
'mistry',
'whiile',
'nah',
'popularised',
'weill',
'gariazzo',
'rojar',
'skewing',
'benetakos',
'deathrow',
'entwistle',
'cebuano',
'hana',
'pragmatist',
'ethnographer',
'matchbox',
'tulkinghorn',
'essandoh',
'juvie',
'heartening',
'wearing',
'pixar',
'panties',
'ills',
'nihilists',
'mulher',
'pleasantness',
'distatefull',
'unisten',
'career',
'dependence',
'vanquished',
'hamatova',
'bolsters',
'timesfunny',
'millenia',
'doe',
'antisocial',
'hound',
'illumination',
'changings',
'compendium',
'helvard',
'cryptozoology',
'earthquake',
'hahahahaha',
'wasted',
'steveday',
'detaining',
'logothethis',
'lumped',
'maltz',
'constructions',
'booger',
'rosalba',
'riverdance',
'arctic',
'recieves',
'parentingwhere',
'kam',
'doppleganger',
'collyer',
'unscripted',
'wan',
'spaak',
'shinning',
'maclhuen',
'incidental',
'ineptly',
'flane',
'invent',
'straightness',
'chirping',
'concorde',
'bracy',
'recording',
'burnout',
'exaggerated',
'ainley',
'rokkuchan',
'unassuming',
'favors',
'colleen',
'assaulting',
'deprecation',
'swallowing',
'feodor',
'ven',
'blammo',
'xine',
'overacts',
'personified',
'logophobia',
'mnard',
'confuse',
'antagonizing',
'arthurian',
'manish',
'hickish',
'joxs',
'extrapolation',
'fattish',
'magnificent',
'temperment',
'popularizing',
'vilification',
'vandyke',
'trifunovic',
'contemporary',
'colorlessly',
'pomerantz',
'boulevardier',
'barzell',
'kafi',
'mutiracial',
'playgroung',
'looping',
'danver',
'leisurely',
'exaggeratedly',
'willow',
'cardinale',
'bumpkins',
'neidhart',
'abstractions',
'monocle',
'movieits',
'epochs',
'axe',
'ovies',
'subtitles',
'reincarnate',
'shrewsbury',
'winding',
'imprisoning',
'guerrilla',
'imprezza',
'dicker',
'figgy',
'repoman',
'milimeters',
'heretics',
'heroo',
'modernity',
'lugging',
'sindhoor',
'mizu',
'postmortem',
'greeting',
'prophetic',
'freuchen',
'mummies',
'napoli',
'seiing',
'blurred',
'alignment',
'junction',
'anniversary',
'yearn',
'howling',
'nearing',
'irrevocably',
'witches',
'budding',
'kardasian',
'glb',
'hately',
'neno',
'baston',
'combined',
'boar',
'vohrer',
'demille',
'crustacean',
'unsub',
'saxophonists',
'lemma',
'journalist',
'fking',
'chimera',
'strum',
'unmoored',
'crossbeams',
'boyish',
'thundercleese',
'disslikes',
'cartwright',
'hounding',
'ideologist',
'putner',
'whap',
'glides',
'deftly',
'taunt',
'tres',
'voiceless',
'apon',
'codenamedragonfly',
'devo',
'reconstituirea',
'promotes',
'voorhees',
'abhi',
'smooth',
'geyser',
'enterntainment',
'vocation',
'sweat',
'midpoint',
'calico',
'refried',
'shakers',
'normality',
'grotesquery',
'flabbergastingly',
'taz',
'miyako',
'plinplin',
'period',
'dernier',
'coherrent',
'providing',
'arena',
'sussanah',
'categorizing',
'chases',
'outsized',
'hooligans',
'hassle',
'suggestion',
'klutz',
'boobage',
'choreographing',
'shi',
'gallantry',
'providence',
'banton',
'scrimm',
'bonhomie',
'tagged',
'envoked',
'dahl',
'bullsh',
'fagan',
'silhouette',
'pitted',
'hedgehog',
'quarrels',
'precarious',
'hepton',
'profundo',
'mileu',
'set',
'pinto',
'acrap',
'rexs',
'onecharacter',
'frenetically',
'bingo',
'backyard',
'backup',
'gems',
'continual',
'sparkly',
'crewmember',
'promo',
'robin',
'acceptance',
'mcneill',
'chalta',
'into',
'deciding',
'channeling',
'trampy',
'intermitable',
'rashness',
'afresh',
'cq',
'franciscus',
'bodybut',
'simpleton',
'wasabi',
'embraceable',
'rake',
'anchorwoman',
'natassja',
'pino',
'reminisces',
'pulcherie',
'jarring',
'tilted',
'accomplishment',
'brighten',
'delli',
'kaoru',
'quizzes',
'crawling',
'wikipedia',
'solder',
'unheated',
'windowless',
'klangs',
'verhoven',
'archetypal',
'stickler',
'realisticly',
'henleys',
'hangout',
'monolithic',
'conspir',
'hardbitten',
'erman',
'mim',
'enlarged',
'dragnet',
'yours',
'caudillos',
'border',
'conviction',
'bewildered',
'endeavour',
'reds',
'ever',
'culminates',
'achieve',
'emanuele',
'tisserand',
'deploy',
'shipbuilding',
'kkk',
'takeovers',
'celler',
'opposition',
'kahn',
'misinforms',
'pumping',
'caetano',
'rajasthani',
'white',
'bevilaqua',
'volleyball',
'engel',
'sarcastic',
'notably',
'expcept',
'bhiku',
'iyer',
'goivernment',
'stars',
'immediate',
'debauchery',
'viel',
'writhed',
'thesis',
'cynical',
'razed',
'stephani',
'teamed',
'prettier',
'cornball',
'britains',
'inconclusive',
'unsuitably',
'explicit',
'scratchy',
'obstructs',
'fraculater',
'altron',
'criminey',
'euphemism',
'knowable',
'cheaper',
'bdus',
'foulkrod',
'lated',
'regimens',
'nikolaev',
'sylvio',
'amish',
'stillman',
'bie',
'earthlings',
'novodny',
'somnambulistic',
'underground',
'bluest',
'souvenir',
'plural',
'anthropophagus',
'plucking',
'cpr',
'summons',
'whelk',
'clocked',
'exult',
'shun',
'swells',
'pulpit',
'wednesdays',
'aeroplane',
'mozes',
'justified',
'morrisette',
'samu',
'rhythymed',
'berth',
'wainwright',
'shainin',
'trude',
'undress',
'halts',
'nascar',
'haavard',
'reignite',
'damsel',
'location',
'applause',
'sham',
'wwaste',
'outshine',
'romasantathe',
'computerized',
'crooks',
'loffe',
'scathed',
'vemork',
'denman',
'dainty',
'hassett',
'broflofski',
'sanctimonious',
'overlay',
'shakespearian',
'karma',
'attainment',
'leontine',
'prominent',
'tapped',
'establishments',
'neutralized',
'monicker',
'behave',
'unafraid',
'theda',
'stealthily',
'smuggle',
'weber',
'schizophreniac',
'lenient',
'embellish',
'brie',
'clytemenstra',
'transfused',
'incipient',
'riddled',
'ii',
'olympian',
'azam',
'nothing',
'sticking',
'powerhouse',
'toffee',
'hornburg',
'mein',
'pathologize',
'agonizes',
'moderators',
'trot',
'montmarte',
'denotes',
'axed',
'manichaean',
'stimulate',
'artem',
'tens',
'dapper',
'fibers',
'qaida',
'precedence',
'value',
'rapeing',
'farse',
'drinks',
'covenant',
'register',
'shaggy',
'wqasn',
'committees',
'lice',
'distractive',
'beauticin',
'glammier',
'robotic',
'gertrude',
'chuke',
'abodes',
'bewitchment',
'pettyjohn',
'patronage',
'browning',
'formally',
'objectionable',
'output',
'vivacious',
'redman',
'fallacious',
'baptized',
'lykis',
'wil',
'freudians',
'psychokinetic',
'marathan',
'kmart',
'uprightness',
'dearable',
'caps',
'guilty',
'glitxy',
'ripped',
'recovers',
'braces',
'mufti',
'unfourtunatly',
'characteratures',
'giorgos',
'viewership',
'perked',
'milieux',
'flicking',
'inmates',
'bicker',
'impropriety',
'scalpels',
'gobsmacked',
'mybluray',
'gills',
'alexandra',
'wingfield',
'goldin',
'innocous',
'fanbase',
'punt',
'humanist',
'tragicomedies',
'freakiness',
'mishandle',
'purer',
'shittier',
'brownings',
'bibbity',
'freddys',
'houseman',
'betrays',
'doncha',
'castings',
'carlos',
'amfortas',
...]

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

In [19]:

import numpy as np

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

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

Out[19]:

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

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

In [20]:

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

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

Out[20]:

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

In [23]:

word2index = {}

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

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

Out[23]:

{'': 0,
'touissant': 1,
'muncey': 2,
'fullscreen': 3,
'manifesting': 4,
'overplaying': 5,
'tents': 6,
'landscapes': 7,
'silicone': 8,
'lalanne': 9,
'unobserved': 10,
'godly': 11,
'jianxiang': 12,
'nachoo': 13,
'acoustics': 14,
'phillimines': 15,
'pummeled': 16,
'ejaculation': 17,
'shugoro': 19,
'substitute': 20,
'turtles': 21,
'shoddily': 22,
'polanski': 23,
'syafie': 24,
'definative': 25,
'gauteng': 26,
'nonstop': 27,
'buccaneering': 28,
'weary': 29,
'kji': 30,
'arrrgghhh': 31,
'agamemnon': 32,
'seaward': 33,
'triste': 34,
'mortimer': 35,
'indict': 36,
'corruption': 37,
'conjunction': 38,
'cineastes': 39,
'hirjee': 40,
'profiles': 41,
'channelling': 42,
'kayak': 43,
'gv': 44,
'hershman': 45,
'decisionsin': 46,
'hemmingway': 47,
'synch': 48,
'walt': 49,
'nob': 50,
'cimarron': 51,
'heisler': 52,
'huxtables': 53,
'juscar': 54,
'funt': 55,
'deterrent': 56,
'scarynot': 57,
'differentiation': 58,
'rewatchability': 59,
'vouch': 60,
'dardino': 61,
'clerking': 62,
'matheisen': 63,
'expeditiously': 64,
'kohara': 66,
'intros': 68,
'dunn': 69,
'construe': 70,
'peacefulness': 71,
'charter': 72,
'bang': 73,
'sergi': 74,
'physically': 75,
'gingernuts': 76,
'definently': 77,
'brasseur': 78,
'ivay': 79,
'ranged': 80,
'reaming': 81,
'leapt': 82,
'shaft': 83,
'segueing': 85,
'hurting': 86,
'goose': 87,
'bonde': 88,
'culled': 89,
'scribbles': 90,
'loris': 91,
'purrrrrrrrrrrrrrrr': 92,
'cameras': 93,
'mischievous': 95,
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'indebtedness': 97,
'submarines': 98,
'glamor': 99,
'appease': 100,
'romanticism': 101,
'hll': 102,
'louda': 103,
'foodstuffs': 104,
'preyer': 105,
'pentameter': 106,
'sourly': 107,
'takers': 108,
'hypersensitive': 109,
'dismantle': 110,
'dza': 112,
'mou': 113,
'snails': 114,
'steely': 115,
'effected': 116,
'feather': 117,
'strangest': 118,
'woodlands': 119,
'ingratiate': 120,
'okey': 121,
'sandbag': 122,
'clowned': 123,
'blackploitation': 124,
'chaptered': 125,
'macbeth': 126,
'verve': 128,
'mbongeni': 129,
'malte': 130,
'fomenting': 131,
'bruhls': 132,
'larded': 134,
'vfcc': 135,
'deewana': 136,
'guptil': 137,
'larp': 138,
'demanding': 139,
'resembled': 140,
'extraterrestrial': 141,
'harni': 142,
'lynched': 143,
'jbl': 144,
'alleging': 145,
'champmathieu': 146,
'determination': 147,
'patron': 148,
'pertain': 149,
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'heterogeneity': 152,
'hiller': 153,
'snippers': 154,
'tc': 155,
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'nordham': 157,
'detritus': 158,
'frogballs': 159,
'gourds': 161,
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'lucian': 164,
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'mahnaz': 167,
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'sega': 169,
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'mindbender': 175,
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'doorpost': 178,
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'maine': 181,
'mandelbaum': 182,
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'troupe': 187,
'lactating': 188,
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'metcalfe': 194,
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'umilak': 196,
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'terrorize': 198,
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'sagr': 216,
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'moretti': 224,
'gring': 225,
'jayston': 226,
'natali': 227,
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'candians': 229,
'allan': 230,
'reabsorbed': 231,
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'sayuri': 237,
'pip': 238,
'duilio': 239,
'significantly': 240,
'norwegia': 241,
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'winged': 243,
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'myspace': 245,
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'mouths': 248,
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'malevolence': 250,
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'bilardo': 252,
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'shaping': 254,
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'households': 257,
'maximimum': 258,
'sssr': 259,
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'farrakhan': 307,
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'stationmaster': 311,
'ugghh': 312,
'swamps': 313,
'hologram': 315,
'whack': 316,
'riffraff': 317,
'withstood': 318,
'confesses': 319,
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'discoverer': 321,
'dewet': 322,
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'detectives': 332,
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'ivanova': 336,
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'maaan': 340,
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'mccathy': 343,
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'disentangling': 351,
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'wgbh': 388,
'haje': 389,
'thriteen': 390,
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'syllable': 395,
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'holt': 398,
'ter': 399,
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'mordantly': 401,
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'doodle': 408,
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'recreate': 410,
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'maclhuen': 497,
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'ven': 518,
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'xine': 520,
'overacts': 521,
'personified': 522,
'logophobia': 523,
'mnard': 524,
'confuse': 525,
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'arthurian': 527,
'manish': 528,
'hickish': 529,
'joxs': 530,
'extrapolation': 531,
'fattish': 532,
'magnificent': 533,
'temperment': 534,
'popularizing': 535,
'vilification': 536,
'vandyke': 537,
'trifunovic': 538,
'contemporary': 539,
'colorlessly': 540,
'pomerantz': 541,
'boulevardier': 542,
'barzell': 543,
'kafi': 544,
'mutiracial': 545,
'playgroung': 546,
'looping': 547,
'danver': 548,
'leisurely': 549,
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'willow': 551,
'cardinale': 552,
'bumpkins': 553,
'neidhart': 554,
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'monocle': 556,
'movieits': 557,
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'subtitles': 561,
'reincarnate': 562,
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'dicker': 568,
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'repoman': 570,
'milimeters': 571,
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'heroo': 573,
'modernity': 574,
'lugging': 575,
'sindhoor': 576,
'mizu': 577,
'postmortem': 578,
'greeting': 579,
'prophetic': 580,
'freuchen': 581,
'mummies': 582,
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'seiing': 584,
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'witches': 594,
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'hately': 598,
'neno': 599,
'baston': 600,
'combined': 601,
'boar': 602,
'vohrer': 603,
'demille': 604,
'crustacean': 605,
'unsub': 606,
'saxophonists': 607,
'lemma': 608,
'journalist': 609,
'fking': 610,
'chimera': 611,
'strum': 612,
'unmoored': 613,
'crossbeams': 614,
'boyish': 615,
'thundercleese': 616,
'disslikes': 617,
'cartwright': 618,
'hounding': 619,
'ideologist': 620,
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'whap': 622,
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'deftly': 624,
'taunt': 625,
'tres': 626,
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'devo': 630,
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'midpoint': 640,
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'taz': 647,
'miyako': 648,
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'dernier': 651,
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'suggestion': 661,
'klutz': 662,
'boobage': 663,
'choreographing': 664,
'shi': 665,
'gallantry': 666,
'providence': 667,
'banton': 668,
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'tagged': 671,
'envoked': 672,
'dahl': 673,
'bullsh': 674,
'fagan': 675,
'silhouette': 676,
'pitted': 677,
'hedgehog': 678,
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'precarious': 680,
'hepton': 681,
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'mileu': 683,
'set': 684,
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'rexs': 688,
'onecharacter': 689,
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'bingo': 691,
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'gems': 694,
'continual': 695,
'sparkly': 696,
'crewmember': 697,
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'robin': 699,
'acceptance': 700,
'mcneill': 701,
'chalta': 702,
'into': 703,
'deciding': 704,
'channeling': 705,
'trampy': 706,
'intermitable': 707,
'rashness': 708,
'afresh': 709,
'cq': 711,
'franciscus': 712,
'bodybut': 713,
'simpleton': 715,
'wasabi': 716,
'embraceable': 717,
'rake': 718,
'anchorwoman': 719,
'natassja': 720,
'pino': 721,
'reminisces': 722,
'pulcherie': 723,
'jarring': 724,
'tilted': 725,
'accomplishment': 726,
'brighten': 727,
'delli': 728,
'kaoru': 729,
'quizzes': 730,
'crawling': 731,
'wikipedia': 732,
'solder': 733,
'unheated': 734,
'windowless': 735,
'klangs': 736,
'verhoven': 737,
'archetypal': 738,
'stickler': 739,
'realisticly': 740,
'henleys': 741,
'hangout': 742,
'monolithic': 743,
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'erman': 746,
'mim': 747,
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'dragnet': 749,
'yours': 750,
'caudillos': 751,
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'bewildered': 754,
'endeavour': 755,
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'tisserand': 761,
'deploy': 762,
'shipbuilding': 763,
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'takeovers': 765,
'celler': 766,
'opposition': 767,
'kahn': 768,
'misinforms': 769,
'pumping': 770,
'caetano': 771,
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'white': 773,
'bevilaqua': 774,
'volleyball': 775,
'engel': 776,
'sarcastic': 777,
'notably': 778,
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'bhiku': 780,
'iyer': 781,
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'stars': 783,
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'viel': 786,
'writhed': 787,
'thesis': 788,
'cynical': 789,
'razed': 790,
'stephani': 791,
'teamed': 792,
'prettier': 793,
'cornball': 794,
'britains': 795,
'inconclusive': 796,
'unsuitably': 797,
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'scratchy': 799,
'obstructs': 801,
'fraculater': 802,
'altron': 803,
'criminey': 804,
'euphemism': 805,
'knowable': 806,
'cheaper': 807,
'bdus': 808,
'foulkrod': 809,
'lated': 810,
'regimens': 811,
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'sylvio': 813,
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'stillman': 815,
'bie': 816,
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'novodny': 819,
'somnambulistic': 820,
'underground': 821,
'bluest': 822,
'souvenir': 823,
'plural': 824,
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'summons': 828,
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'clocked': 830,
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'swells': 833,
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'mozes': 837,
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'morrisette': 839,
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'reignite': 850,
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'toffee': 899,
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'mein': 902,
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'moderators': 905,
'trot': 906,
'montmarte': 907,
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'manichaean': 910,
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'tens': 914,
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'drinks': 922,
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'glammier': 931,
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'gertrude': 933,
'chuke': 934,
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'vivacious': 943,
'redman': 944,
'fallacious': 945,
'baptized': 946,
'lykis': 947,
'wil': 948,
'freudians': 949,
'psychokinetic': 950,
'marathan': 951,
'kmart': 952,
'uprightness': 953,
'dearable': 954,
'caps': 955,
'guilty': 956,
'glitxy': 957,
'ripped': 958,
'recovers': 959,
'braces': 960,
'mufti': 961,
'unfourtunatly': 962,
'characteratures': 963,
'giorgos': 964,
'viewership': 965,
'perked': 966,
'milieux': 967,
'flicking': 968,
'inmates': 969,
'bicker': 970,
'impropriety': 971,
'scalpels': 972,
'gobsmacked': 973,
'mybluray': 974,
'gills': 975,
'alexandra': 976,
'wingfield': 977,
'goldin': 978,
'innocous': 979,
'fanbase': 980,
'punt': 981,
'humanist': 982,
'tragicomedies': 983,
'freakiness': 985,
'mishandle': 986,
'purer': 987,
'shittier': 988,
'brownings': 989,
'bibbity': 990,
'freddys': 991,
'houseman': 992,
'betrays': 994,
'doncha': 995,
'castings': 997,
'carlos': 998,
'amfortas': 999,
...}

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

In [24]:

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 [25]:

layer_0

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

Out[25]:

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

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

In [26]:

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

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

In [27]:

labels[0]

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

Out[27]:

'POSITIVE'

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

In [28]:

get_target_for_label(labels[0])

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

Out[28]:

1

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

In [29]:

labels[1]

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

Out[29]:

'NEGATIVE'

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

In [30]:

get_target_for_label(labels[1])

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

Out[30]:

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 [31]:

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 [87]:

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

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

In [61]:

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

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

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

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

In [62]:

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

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

Progress:0.0% Speed(reviews/sec):0.0 #Correct:0 #Trained:1 Training Accuracy:0.0%
Progress:10.4% Speed(reviews/sec):89.58 #Correct:1250 #Trained:2501 Training Accuracy:49.9%
Progress:20.8% Speed(reviews/sec):95.03 #Correct:2500 #Trained:5001 Training Accuracy:49.9%
Progress:27.4% Speed(reviews/sec):95.46 #Correct:3295 #Trained:6592 Training Accuracy:49.9%

---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
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 [32]:

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

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

Out[32]:

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

In [33]:

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 [34]:

layer_0

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

Out[34]:

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

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

In [35]:

review_counter = Counter()

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

In [36]:

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

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

In [37]:

review_counter.most_common()

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

Out[37]:

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

``````

# Project 4: Reducing Noise in our Input Data

``````

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 [40]:

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

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

In [41]:

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):107.3 #Correct:1816 #Trained:2501 Training Accuracy:72.6%
Progress:20.8% Speed(reviews/sec):118.3 #Correct:3796 #Trained:5001 Training Accuracy:75.9%
Progress:31.2% Speed(reviews/sec):127.0 #Correct:5884 #Trained:7501 Training Accuracy:78.4%
Progress:41.6% Speed(reviews/sec):131.7 #Correct:8021 #Trained:10001 Training Accuracy:80.2%
Progress:52.0% Speed(reviews/sec):134.7 #Correct:10158 #Trained:12501 Training Accuracy:81.2%
Progress:62.5% Speed(reviews/sec):136.8 #Correct:12287 #Trained:15001 Training Accuracy:81.9%
Progress:72.9% Speed(reviews/sec):138.3 #Correct:14398 #Trained:17501 Training Accuracy:82.2%
Progress:83.3% Speed(reviews/sec):139.3 #Correct:16572 #Trained:20001 Training Accuracy:82.8%
Progress:93.7% Speed(reviews/sec):140.2 #Correct:18755 #Trained:22501 Training Accuracy:83.3%
Progress:99.9% Speed(reviews/sec):140.7 #Correct:20077 #Trained:24000 Training Accuracy:83.6%

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

In [42]:

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

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

Progress:99.9% Speed(reviews/sec):540.6% #Correct:859 #Tested:1000 Testing Accuracy:85.9%

``````