# 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 [3]:

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 [4]:

len(reviews)

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

Out[4]:

25000

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

In [5]:

reviews[0]

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

Out[5]:

'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 [6]:

labels[0]

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

Out[6]:

'POSITIVE'

``````

# Lesson: Develop a Predictive Theory

``````

In [7]:

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 [8]:

from collections import Counter
import numpy as np

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

In [9]:

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

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

In [10]:

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 [11]:

positive_counts.most_common()

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

Out[11]:

[('', 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 [12]:

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 [13]:

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

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

Out[13]:

[('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 [14]:

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

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

Out[14]:

[('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 [15]:

from IPython.display import Image

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

Image(filename='sentiment_network.png')

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

Out[15]:

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

In [16]:

review = "The movie was excellent"

Image(filename='sentiment_network_pos.png')

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

Out[16]:

``````

# 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]:

['',
'uncreative',
'tink',
'mcnicol',
'baldly',
'muffins',
'judeo',
'attention',
'accelerate',
'monkees',
'impressionistic',
'gaol',
'oyster',
'doyeon',
'mechanism',
'affinity',
'fictionalizing',
'insipid',
'meecy',
'nekron',
'blouses',
'salvageable',
'diaphanous',
'crotch',
'habitat',
'comms',
'picard',
'gator',
'lanchester',
'holmfrid',
'animations',
'charcters',
'automag',
'greenscreen',
'cameroonian',
'lilt',
'gravelings',
'hardiman',
'idyllic',
'mathew',
'tuxedo',
'bailor',
'eek',
'lumieres',
'grayce',
'authorities',
'kidnappings',
'hackensack',
'bergenon',
'mansion',
'exert',
'settee',
'unearthed',
'deesh',
'swooningly',
'specializing',
'altough',
'grungy',
'trendy',
'recedes',
'supercilious',
'chorines',
'earthier',
'defibulator',
'massaccesi',
'wolhiem',
'recapitulates',
'alumni',
'chloe',
'firebug',
'daysthis',
'then',
'doot',
'nicaragua',
'kaye',
'stalwarts',
'dorset',
'swank',
'glamorous',
'voigt',
'narration',
'sexism',
'juarezon',
'eliana',
'misguiding',
'prunes',
'anatomically',
'cows',
'park',
'sophocles',
'ender',
'goals',
'noise',
'governing',
'persona',
'mencia',
'randon',
'cundey',
'sleuth',
'tentacled',
'jaan',
'boen',
'verite',
'styne',
'pictograms',
'thumbnail',
'discriminates',
'reognise',
'kazzam',
'womanhood',
'dornwinkles',
'borga',
'sundae',
'imom',
'deathline',
'females',
'bonin',
'enterntainment',
'dre',
'suxor',
'confrontation',
'contents',
'jrgen',
'improbably',
'unquiet',
'operandi',
'frankie',
'junctures',
'products',
'flagg',
'ultimatums',
'fetching',
'pussies',
'backstabber',
'hodder',
'impact',
'corset',
'extirpate',
'industrialist',
'sedimentation',
'purefoy',
'reverses',
'lung',
'coaxed',
'pricks',
'emptour',
'hyland',
'kinfolk',
'shafts',
'laureen',
'hurracanrana',
'buckwheat',
'russsia',
'helin',
'response',
'gayle',
'renee',
'miscalculated',
'rush',
'noisy',
'shoeing',
'tragi',
'rugby',
'drugsas',
'stops',
'niccolo',
'sentient',
'horner',
'carrie',
'tlog',
'lau',
'turner',
'dhawan',
'henze',
'nobudget',
'roemenian',
'yields',
'fanny',
'diffident',
'mastercard',
'winier',
'prospecting',
'mopsy',
'luego',
'cooter',
'river',
'spraypainted',
'tediousness',
'bhat',
'bohemia',
'inauguration',
'toomey',
'playgroud',
'artiness',
'barley',
'shouted',
'edies',
'wardrobes',
'hurries',
'pawnshop',
'voicetrack',
'reconfirmed',
'lasorda',
'engineer',
'hoodwinked',
'vamsi',
'priding',
'dyana',
'spagnolo',
'dilettante',
'norway',
'dewan',
'grandkid',
'gorefest',
'handle',
'unmatchable',
'sg',
'eburne',
'breathed',
'repoire',
'gas',
'baptism',
'masterbates',
'tenderizer',
'kwami',
'publicity',
'oosh',
'luzhini',
'detecting',
'vies',
'ixpe',
'monies',
'yeun',
'matthieu',
'aussie',
'teru',
'initiating',
'mythological',
'doughnut',
'hogtied',
'jakub',
'tuskegee',
'onwhich',
'bajpai',
'homeland',
'highen',
'acclaim',
'eames',
'leeli',
'entombment',
'opted',
'kitrosser',
'creely',
'bowry',
'stalled',
'muncey',
'unoutstanding',
'swang',
'ej',
'suuuuuuuuuuuucks',
'major',
'sergeant',
'coats',
'spacemen',
'reviczky',
'zannuck',
'chatty',
'slur',
'starched',
'indicates',
'dyeing',
'chalo',
'henpecked',
'amature',
'equalizer',
'whoooole',
'misjudgement',
'tpgtc',
'morphing',
'cudos',
'derivative',
'clunes',
'aster',
'arguments',
'expeditioners',
'chu',
'unmerciful',
'banjos',
'cloths',
'lenoir',
'teenkill',
'podges',
'mihaela',
'strasbourg',
'babbs',
'naidu',
'confederation',
'klick',
'flroiane',
'railways',
'suavity',
'roamer',
'flood',
'installations',
'atemp',
'twins',
'mcdowell',
'frauded',
'schildkraut',
'handwork',
'charmer',
'gotcha',
'amateurs',
'wath',
'modulated',
'capably',
'pulpy',
'orisha',
'inventing',
'frayed',
'telepathic',
'bribery',
'ooout',
'rethwisch',
'haul',
'ricca',
'retract',
'amount',
'everywhere',
'woodlands',
'unplanned',
'opinion',
'jouanneau',
'glands',
'jealousy',
'jingle',
'jammed',
'sacked',
'ravaged',
'plagiarised',
'conspicuous',
'babhi',
'buice',
'unbearableness',
'dishwater',
'decomposition',
'turquoise',
'funimation',
'cackle',
'disposing',
'submerging',
'sedatives',
'bandmates',
'danes',
'masterful',
'fuhrer',
'polarized',
'endorsing',
'pigalle',
'mumps',
'gehrlich',
'nicolosi',
'burundi',
'crusty',
'eliminated',
'illustriousness',
'bw',
'maman',
'chinpira',
'bench',
'medicated',
'fyrom',
'fckin',
'chewy',
'heavens',
'protect',
'fuente',
'katelyn',
'gem',
'poste',
'verdicts',
'bandwidth',
'mercurially',
'peek',
'var',
'leeringly',
'pmrc',
'tenet',
'dismemberment',
'georgeous',
'whatnot',
'nia',
'boringlane',
'artel',
'shu',
'frankly',
'bishop',
'marred',
'allover',
'magilla',
'patti',
'tosa',
'atmos',
'gulp',
'stacy',
'imhotep',
'anisio',
'galapagos',
'pellew',
'tually',
'shotgunning',
'revisitation',
'mistress',
'macao',
'indecisively',
'immeasurably',
'tyres',
'almoust',
'cinenephile',
'roughly',
'flow',
'terminators',
'stepehn',
'wreaking',
'gurus',
'buzzell',
'bing',
'transsylvanian',
'throughly',
'kameena',
'chamberland',
'breckin',
'bisto',
'knowledgeable',
'conductors',
'flips',
'whitfield',
'azjazz',
'excels',
'bodo',
'promicing',
'riedelsheimer',
'spoonful',
'complemented',
'vulcan',
'heaton',
'incidence',
'catched',
'virginny',
'repellently',
'justia',
'incorruptable',
'offenders',
'bonneville',
'devastate',
'nabucco',
'expand',
'speilberg',
'fetid',
'bowling',
'vessel',
'essanay',
'tess',
'moraka',
'polygram',
'brinda',
'invent',
'monte',
'homey',
'ddr',
'munoz',
'synthed',
'poorest',
'tonks',
'replacdmetn',
'congregations',
'commenter',
'goggles',
'shrine',
'ttm',
'fined',
'override',
'wonderley',
'merika',
'instability',
'fillion',
'gallagher',
'akshaya',
'pasteur',
'arlette',
'mitevska',
'kent',
'rite',
'governator',
'dominic',
'kajol',
'bartholomew',
'ensconced',
'mommas',
'suraj',
'elanor',
'relegated',
'starrer',
'pharaoh',
'nepolean',
'howson',
'ortelli',
'cynically',
'caan',
'coaxing',
'brie',
'unflinching',
'browses',
'byword',
'thunderbirds',
'satisying',
'downhill',
'bellocchio',
'outperform',
'argonautica',
'urgency',
'crawly',
'model',
'somersaults',
'indelicate',
'joining',
'loaned',
'hornblower',
'foley',
'luting',
'iconoclastic',
'picturization',
'frowns',
'battery',
'choke',
'patented',
'pedtrchenko',
'neal',
'certified',
'secretly',
'turman',
'morgues',
'jody',
'subconsciously',
'applicant',
'oar',
'concentrates',
'mick',
'barebones',
'commercisliation',
'rajni',
'jag',
'attainment',
'butterick',
'ut',
'ossessione',
'odete',
'malnourished',
'parkers',
'nikolaj',
'its',
'sauna',
'wyat',
'poupard',
'concerns',
'guitars',
'acropolis',
'afghani',
'wonderment',
'canutt',
'gabel',
'iceman',
'byniarski',
'cant',
'borderlines',
'eighty',
'magnates',
'tickles',
'robo',
'dampening',
'eur',
'rumiko',
'spouted',
'gyppos',
'linz',
'divinities',
'origin',
'fragrance',
'amazingfrom',
'bookworm',
'softshoe',
'limbless',
'maris',
'pursuit',
'golly',
'megaeuros',
'zucker',
'roquevert',
'fantasising',
'song',
'palette',
'humpback',
'kings',
'intimately',
'figaro',
'grillo',
'unsightly',
'depardieu',
'rheubottom',
'weightless',
'bushes',
'cheesily',
'cheesiness',
'diaper',
'bartender',
'pavarotti',
'annivesery',
'maneuver',
'yoshida',
'roughing',
'plump',
'shipmates',
'squalid',
'vingana',
'emigrates',
'cold',
'feffer',
'densest',
'bresslaw',
'conveyed',
'lenore',
'invigorated',
'knickers',
'invests',
'heero',
'hanoverian',
'cosy',
'dody',
'eleanor',
'kuno',
'requirements',
'hoven',
'educates',
'emotionless',
'toshiyuki',
'naswip',
'claustraphobia',
'friendless',
'collaborates',
'connections',
'soisson',
'repaid',
'bleeder',
'finley',
'scorning',
'rattlesnake',
'peripatetic',
'sunburst',
'ministers',
'restart',
'spheres',
'protocols',
'tang',
'cederic',
'vapid',
'tilman',
'extort',
'starfighter',
'feistyness',
'pseudonym',
'perceives',
'throb',
'griffit',
'loserto',
'blalack',
'reposes',
'gallon',
'connally',
'mumbai',
'classically',
'freakazoid',
'durring',
'heals',
'lollies',
'luv',
'rocker',
'transexual',
'enfance',
'forbidden',
'actuality',
'fussy',
'boobtube',
'ja',
'berner',
'razrukha',
'kaboud',
'aroused',
'corpsified',
'obelisk',
'dismay',
'wealthy',
'kosmos',
'diabolically',
'stk',
'sachetti',
'jongchan',
'stables',
'immigrant',
'cp',
'integration',
'janitor',
'egotistic',
'terriers',
'aimanov',
'hoop',
'afilm',
'blasts',
'waster',
'zealous',
'lamo',
'oro',
'chipmunks',
'episodes',
'baffling',
'roaches',
'sicily',
'brochure',
'buttons',
'tremain',
'impressive',
'obscence',
'porely',
'bartley',
'immunity',
'midriff',
'haunts',
'atlantian',
'pained',
'nough',
'pouty',
'nekhron',
'nz',
'appolonia',
'axel',
'marshalls',
'affects',
'ying',
'eeriest',
'swigging',
'furgusson',
'untergang',
'jewelry',
'romanticizing',
'artifacts',
'freebasing',
'brown',
'dareus',
'carltio',
'performaces',
'uncorruptable',
'expressionless',
'meera',
'intermesh',
'searching',
'amuses',
'ization',
'ohio',
'feckless',
'lapyuta',
'endangered',
'grabbers',
'juicy',
'undergarments',
'sartor',
'eko',
'floated',
'cullen',
'demonio',
'dinghy',
'appreciable',
'rafifi',
'personals',
'ravishing',
'classmate',
'tupinambas',
'useless',
'machetes',
'veight',
'regenerate',
'sprinkled',
'darting',
'unnervingly',
'sicken',
'hourglass',
'vierde',
'megazones',
'luthor',
'audacious',
'eggotistical',
'teer',
'prozess',
'scientologists',
'naboombu',
'rocque',
'toi',
'nozires',
'frikkin',
'intended',
'gall',
'operating',
'whoah',
'nashville',
'thank',
'maids',
'moderate',
'novodny',
'molestation',
'lawrence',
'shaker',
'demnio',
'hoshi',
'directly',
'torpedoed',
'reitman',
'humphrey',
'errors',
'mendez',
'sincerity',
'teletubbies',
'comity',
'dachsund',
'farzetta',
'lackey',
'monogamistic',
'ipod',
'quitting',
'prjean',
'boober',
'ogles',
'peculiarities',
'godamnawful',
'gospels',
'customs',
'hooknose',
'eds',
'brontean',
'rou',
'gr',
'dagoba',
'ryall',
'convincing',
'approach',
'marginal',
'birnley',
'stedicam',
'dabbi',
'redecorating',
'feb',
'developmental',
'flagitious',
'anew',
'sprinted',
'raphel',
'retch',
'historicaly',
'lees',
'guerin',
'professions',
'poulain',
'emanated',
'goodnik',
'concerto',
'andrew',
'daffily',
'fireplaces',
'discomfited',
'ballistic',
'thunderously',
'alumnus',
'rodentz',
'briley',
'regarded',
'keymaster',
'haig',
'untied',
'marriage',
'transamerica',
'elektra',
'buchfellner',
'quakerly',
'acquired',
'lobotomized',
'breslin',
'bravora',
'unknowable',
'barbirino',
'makepeace',
'daffy',
'bergmanesque',
'teamsters',
'annick',
'gostoso',
'rakowsky',
'enzo',
'why',
'witted',
'favour',
'scholar',
'reaped',
'pacifier',
'mccombs',
'agent',
'wisecracker',
'superfical',
'mujhe',
'cultured',
'hein',
'norden',
'besiege',
'pelicangs',
'buttress',
'leavitt',
'gu',
'wolfstein',
'brusque',
'pieces',
'uncouth',
'strtebeker',
'interval',
'lustreless',
'greydon',
'plata',
'bhagyashree',
'masiela',
'brandos',
'abo',
'loaner',
'stapled',
'forslani',
'vindicate',
'cripple',
'dem',
'artistical',
'avec',
'hollwyood',
'heist',
'katch',
'atmosphereic',
'zippo',
'disengorges',
'mtro',
'chewie',
'renata',
'technicalities',
'mde',
'gerard',
'renea',
'titillated',
'fictionalized',
'notethe',
'loansharks',
'invigorates',
'payoff',
'kuriyami',
'relentlessy',
...]

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

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 [21]:

word2index = {}

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

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

Out[21]:

{'': 0,
'uncreative': 1,
'tink': 2,
'mcnicol': 3,
'baldly': 4,
'muffins': 5,
'judeo': 6,
'attention': 7,
'accelerate': 8,
'monkees': 9,
'impressionistic': 10,
'gaol': 11,
'oyster': 12,
'doyeon': 13,
'mechanism': 14,
'affinity': 15,
'fictionalizing': 16,
'insipid': 17,
'meecy': 18,
'nekron': 19,
'blouses': 20,
'salvageable': 21,
'diaphanous': 22,
'crotch': 23,
'habitat': 24,
'comms': 25,
'picard': 26,
'gator': 27,
'lanchester': 28,
'holmfrid': 29,
'animations': 30,
'charcters': 31,
'automag': 32,
'greenscreen': 33,
'cameroonian': 34,
'lilt': 35,
'gravelings': 36,
'hardiman': 37,
'idyllic': 38,
'mathew': 39,
'tuxedo': 40,
'bailor': 41,
'eek': 42,
'lumieres': 43,
'grayce': 44,
'authorities': 45,
'kidnappings': 46,
'hackensack': 47,
'bergenon': 48,
'mansion': 49,
'exert': 50,
'settee': 51,
'unearthed': 53,
'deesh': 54,
'swooningly': 55,
'specializing': 56,
'altough': 57,
'grungy': 58,
'trendy': 59,
'recedes': 60,
'supercilious': 61,
'chorines': 62,
'earthier': 63,
'defibulator': 64,
'massaccesi': 65,
'wolhiem': 66,
'recapitulates': 67,
'alumni': 68,
'chloe': 69,
'firebug': 70,
'daysthis': 71,
'then': 72,
'doot': 73,
'nicaragua': 74,
'kaye': 75,
'stalwarts': 76,
'dorset': 77,
'swank': 78,
'glamorous': 79,
'voigt': 80,
'narration': 81,
'sexism': 82,
'juarezon': 83,
'eliana': 84,
'misguiding': 85,
'prunes': 86,
'anatomically': 87,
'cows': 88,
'park': 89,
'sophocles': 90,
'ender': 91,
'goals': 92,
'noise': 93,
'governing': 94,
'persona': 95,
'mencia': 96,
'randon': 97,
'cundey': 98,
'sleuth': 99,
'tentacled': 100,
'jaan': 101,
'boen': 102,
'verite': 103,
'styne': 104,
'pictograms': 105,
'thumbnail': 106,
'discriminates': 107,
'reognise': 108,
'kazzam': 109,
'womanhood': 110,
'dornwinkles': 111,
'borga': 112,
'sundae': 113,
'imom': 114,
'deathline': 115,
'females': 116,
'bonin': 117,
'enterntainment': 118,
'dre': 119,
'suxor': 120,
'confrontation': 121,
'contents': 122,
'jrgen': 123,
'improbably': 124,
'unquiet': 125,
'operandi': 126,
'frankie': 127,
'junctures': 128,
'products': 130,
'flagg': 131,
'ultimatums': 132,
'fetching': 133,
'pussies': 134,
'backstabber': 135,
'hodder': 136,
'impact': 137,
'corset': 138,
'extirpate': 139,
'industrialist': 140,
'sedimentation': 141,
'purefoy': 142,
'reverses': 143,
'lung': 145,
'coaxed': 146,
'pricks': 147,
'emptour': 148,
'hyland': 149,
'kinfolk': 150,
'shafts': 151,
'laureen': 152,
'hurracanrana': 153,
'buckwheat': 154,
'russsia': 155,
'helin': 156,
'response': 157,
'gayle': 158,
'renee': 159,
'miscalculated': 160,
'rush': 161,
'noisy': 162,
'shoeing': 163,
'tragi': 164,
'rugby': 165,
'drugsas': 166,
'stops': 168,
'niccolo': 169,
'sentient': 170,
'horner': 171,
'carrie': 172,
'tlog': 173,
'lau': 174,
'turner': 175,
'dhawan': 176,
'henze': 177,
'nobudget': 178,
'roemenian': 179,
'yields': 180,
'fanny': 181,
'diffident': 182,
'mastercard': 183,
'winier': 184,
'prospecting': 185,
'mopsy': 186,
'luego': 187,
'cooter': 188,
'river': 189,
'spraypainted': 190,
'tediousness': 191,
'bhat': 192,
'bohemia': 193,
'inauguration': 194,
'toomey': 195,
'playgroud': 196,
'artiness': 197,
'barley': 198,
'shouted': 199,
'edies': 201,
'wardrobes': 202,
'hurries': 203,
'pawnshop': 204,
'voicetrack': 205,
'reconfirmed': 206,
'lasorda': 207,
'engineer': 208,
'hoodwinked': 209,
'vamsi': 210,
'priding': 211,
'dyana': 212,
'spagnolo': 213,
'dilettante': 214,
'norway': 215,
'dewan': 216,
'grandkid': 217,
'gorefest': 218,
'handle': 219,
'unmatchable': 220,
'sg': 221,
'eburne': 222,
'breathed': 223,
'repoire': 224,
'gas': 225,
'baptism': 226,
'masterbates': 227,
'tenderizer': 228,
'kwami': 229,
'publicity': 230,
'oosh': 231,
'luzhini': 232,
'detecting': 233,
'vies': 234,
'ixpe': 235,
'monies': 236,
'yeun': 237,
'matthieu': 238,
'aussie': 239,
'teru': 240,
'initiating': 243,
'mythological': 245,
'doughnut': 246,
'hogtied': 247,
'jakub': 248,
'tuskegee': 249,
'onwhich': 250,
'bajpai': 251,
'homeland': 252,
'highen': 253,
'acclaim': 254,
'eames': 255,
'leeli': 256,
'entombment': 257,
'opted': 259,
'kitrosser': 260,
'creely': 261,
'bowry': 262,
'stalled': 263,
'muncey': 264,
'unoutstanding': 265,
'swang': 266,
'ej': 267,
'suuuuuuuuuuuucks': 268,
'major': 269,
'sergeant': 270,
'coats': 271,
'spacemen': 272,
'reviczky': 273,
'zannuck': 274,
'chatty': 275,
'slur': 276,
'starched': 277,
'indicates': 278,
'dyeing': 279,
'chalo': 280,
'henpecked': 281,
'amature': 282,
'equalizer': 283,
'whoooole': 284,
'misjudgement': 285,
'tpgtc': 286,
'morphing': 287,
'cudos': 288,
'derivative': 289,
'clunes': 290,
'aster': 291,
'arguments': 293,
'expeditioners': 294,
'chu': 295,
'unmerciful': 296,
'banjos': 297,
'cloths': 298,
'lenoir': 299,
'teenkill': 300,
'podges': 301,
'mihaela': 303,
'strasbourg': 305,
'babbs': 306,
'naidu': 308,
'confederation': 309,
'klick': 310,
'flroiane': 311,
'railways': 312,
'suavity': 313,
'roamer': 314,
'flood': 315,
'installations': 316,
'atemp': 317,
'twins': 318,
'mcdowell': 319,
'frauded': 320,
'schildkraut': 321,
'handwork': 322,
'charmer': 323,
'gotcha': 324,
'amateurs': 325,
'wath': 326,
'modulated': 327,
'capably': 328,
'pulpy': 329,
'orisha': 330,
'inventing': 331,
'frayed': 332,
'telepathic': 333,
'bribery': 334,
'ooout': 335,
'rethwisch': 336,
'haul': 337,
'ricca': 338,
'retract': 339,
'amount': 340,
'everywhere': 341,
'woodlands': 342,
'unplanned': 343,
'opinion': 345,
'jouanneau': 346,
'glands': 347,
'jealousy': 348,
'jingle': 349,
'jammed': 350,
'sacked': 351,
'ravaged': 352,
'plagiarised': 353,
'conspicuous': 354,
'babhi': 355,
'buice': 356,
'unbearableness': 357,
'dishwater': 358,
'decomposition': 359,
'turquoise': 360,
'funimation': 361,
'cackle': 362,
'disposing': 363,
'submerging': 364,
'sedatives': 365,
'bandmates': 366,
'danes': 367,
'masterful': 368,
'fuhrer': 369,
'polarized': 370,
'endorsing': 371,
'pigalle': 372,
'mumps': 373,
'gehrlich': 374,
'nicolosi': 375,
'burundi': 376,
'crusty': 377,
'eliminated': 378,
'illustriousness': 379,
'bw': 380,
'maman': 381,
'chinpira': 382,
'bench': 383,
'medicated': 384,
'fyrom': 385,
'fckin': 386,
'chewy': 387,
'heavens': 388,
'protect': 389,
'fuente': 390,
'katelyn': 391,
'gem': 392,
'poste': 393,
'verdicts': 394,
'bandwidth': 395,
'mercurially': 396,
'peek': 397,
'var': 398,
'leeringly': 399,
'pmrc': 400,
'tenet': 401,
'dismemberment': 402,
'georgeous': 404,
'whatnot': 405,
'nia': 406,
'boringlane': 407,
'artel': 408,
'shu': 409,
'frankly': 410,
'bishop': 411,
'marred': 412,
'allover': 413,
'magilla': 414,
'patti': 415,
'tosa': 416,
'atmos': 417,
'gulp': 418,
'stacy': 419,
'imhotep': 420,
'anisio': 421,
'galapagos': 422,
'pellew': 424,
'tually': 425,
'shotgunning': 426,
'revisitation': 427,
'mistress': 429,
'macao': 430,
'indecisively': 431,
'immeasurably': 432,
'tyres': 433,
'almoust': 434,
'cinenephile': 435,
'roughly': 436,
'flow': 437,
'terminators': 438,
'stepehn': 439,
'wreaking': 440,
'gurus': 441,
'buzzell': 442,
'bing': 443,
'transsylvanian': 444,
'throughly': 445,
'kameena': 446,
'chamberland': 447,
'breckin': 448,
'bisto': 449,
'knowledgeable': 450,
'conductors': 451,
'flips': 452,
'whitfield': 453,
'azjazz': 454,
'excels': 455,
'bodo': 456,
'promicing': 457,
'riedelsheimer': 458,
'spoonful': 459,
'complemented': 460,
'vulcan': 462,
'heaton': 463,
'incidence': 464,
'catched': 465,
'virginny': 466,
'repellently': 467,
'justia': 468,
'incorruptable': 469,
'offenders': 470,
'bonneville': 471,
'devastate': 472,
'nabucco': 473,
'expand': 474,
'speilberg': 475,
'fetid': 476,
'bowling': 477,
'vessel': 478,
'essanay': 479,
'tess': 480,
'moraka': 481,
'polygram': 482,
'brinda': 483,
'invent': 484,
'monte': 485,
'homey': 486,
'ddr': 487,
'munoz': 488,
'synthed': 489,
'poorest': 490,
'tonks': 491,
'replacdmetn': 492,
'congregations': 493,
'commenter': 494,
'goggles': 495,
'shrine': 496,
'ttm': 497,
'fined': 498,
'override': 499,
'wonderley': 500,
'merika': 501,
'instability': 502,
'fillion': 503,
'gallagher': 504,
'akshaya': 505,
'pasteur': 506,
'arlette': 507,
'mitevska': 508,
'kent': 509,
'rite': 510,
'governator': 511,
'dominic': 513,
'kajol': 514,
'bartholomew': 515,
'ensconced': 516,
'mommas': 517,
'suraj': 518,
'elanor': 519,
'relegated': 520,
'starrer': 521,
'pharaoh': 522,
'nepolean': 523,
'howson': 524,
'ortelli': 525,
'cynically': 526,
'caan': 527,
'coaxing': 528,
'brie': 529,
'unflinching': 531,
'browses': 532,
'byword': 533,
'thunderbirds': 534,
'satisying': 535,
'downhill': 536,
'bellocchio': 537,
'outperform': 538,
'argonautica': 539,
'urgency': 540,
'crawly': 542,
'model': 543,
'somersaults': 544,
'indelicate': 545,
'joining': 546,
'loaned': 547,
'hornblower': 548,
'foley': 549,
'luting': 550,
'iconoclastic': 551,
'picturization': 552,
'frowns': 553,
'battery': 554,
'choke': 555,
'patented': 556,
'pedtrchenko': 557,
'neal': 558,
'certified': 559,
'secretly': 560,
'turman': 561,
'morgues': 562,
'jody': 563,
'subconsciously': 564,
'applicant': 565,
'oar': 566,
'concentrates': 567,
'mick': 568,
'barebones': 569,
'commercisliation': 570,
'rajni': 571,
'jag': 572,
'attainment': 573,
'butterick': 574,
'ut': 575,
'ossessione': 576,
'odete': 577,
'malnourished': 578,
'parkers': 579,
'nikolaj': 580,
'its': 581,
'sauna': 582,
'wyat': 583,
'poupard': 584,
'concerns': 585,
'guitars': 586,
'acropolis': 587,
'afghani': 588,
'wonderment': 589,
'canutt': 590,
'gabel': 591,
'iceman': 592,
'byniarski': 593,
'cant': 594,
'borderlines': 595,
'eighty': 596,
'magnates': 597,
'tickles': 598,
'robo': 599,
'dampening': 600,
'eur': 601,
'rumiko': 602,
'spouted': 603,
'gyppos': 604,
'linz': 605,
'divinities': 606,
'origin': 607,
'fragrance': 608,
'amazingfrom': 609,
'bookworm': 610,
'softshoe': 611,
'limbless': 612,
'maris': 613,
'pursuit': 614,
'golly': 615,
'megaeuros': 616,
'zucker': 617,
'roquevert': 618,
'fantasising': 619,
'song': 620,
'palette': 621,
'humpback': 622,
'kings': 623,
'intimately': 624,
'figaro': 625,
'grillo': 626,
'unsightly': 627,
'depardieu': 628,
'rheubottom': 630,
'weightless': 631,
'bushes': 632,
'cheesily': 633,
'cheesiness': 634,
'diaper': 635,
'bartender': 636,
'pavarotti': 637,
'annivesery': 638,
'maneuver': 640,
'yoshida': 641,
'roughing': 642,
'plump': 643,
'shipmates': 644,
'squalid': 645,
'vingana': 646,
'emigrates': 647,
'cold': 648,
'feffer': 649,
'densest': 650,
'bresslaw': 651,
'conveyed': 652,
'lenore': 653,
'invigorated': 654,
'knickers': 655,
'invests': 656,
'heero': 657,
'hanoverian': 658,
'cosy': 659,
'dody': 660,
'eleanor': 661,
'kuno': 662,
'requirements': 663,
'hoven': 665,
'educates': 666,
'emotionless': 667,
'toshiyuki': 668,
'naswip': 669,
'claustraphobia': 671,
'friendless': 672,
'collaborates': 673,
'connections': 674,
'soisson': 675,
'repaid': 676,
'bleeder': 677,
'finley': 678,
'scorning': 679,
'rattlesnake': 680,
'peripatetic': 681,
'sunburst': 682,
'ministers': 683,
'restart': 684,
'spheres': 685,
'protocols': 687,
'tang': 688,
'cederic': 689,
'vapid': 690,
'tilman': 691,
'extort': 692,
'starfighter': 693,
'feistyness': 694,
'pseudonym': 695,
'perceives': 696,
'throb': 697,
'griffit': 698,
'loserto': 699,
'blalack': 700,
'reposes': 701,
'gallon': 702,
'connally': 703,
'mumbai': 704,
'classically': 705,
'freakazoid': 706,
'durring': 707,
'heals': 708,
'lollies': 709,
'luv': 710,
'rocker': 711,
'transexual': 712,
'enfance': 713,
'forbidden': 714,
'actuality': 715,
'fussy': 716,
'boobtube': 717,
'ja': 718,
'berner': 719,
'razrukha': 720,
'kaboud': 721,
'aroused': 722,
'corpsified': 723,
'obelisk': 724,
'dismay': 725,
'wealthy': 726,
'kosmos': 727,
'diabolically': 728,
'stk': 730,
'sachetti': 731,
'jongchan': 732,
'stables': 733,
'immigrant': 734,
'cp': 735,
'integration': 736,
'janitor': 737,
'egotistic': 738,
'terriers': 739,
'aimanov': 740,
'hoop': 741,
'afilm': 742,
'blasts': 743,
'waster': 745,
'zealous': 747,
'lamo': 748,
'oro': 750,
'chipmunks': 751,
'episodes': 752,
'baffling': 753,
'roaches': 754,
'sicily': 755,
'brochure': 756,
'buttons': 757,
'tremain': 758,
'impressive': 759,
'obscence': 760,
'porely': 761,
'bartley': 762,
'immunity': 763,
'midriff': 764,
'haunts': 765,
'atlantian': 766,
'pained': 767,
'nough': 768,
'pouty': 769,
'nekhron': 770,
'nz': 771,
'appolonia': 772,
'axel': 773,
'marshalls': 774,
'affects': 775,
'ying': 776,
'eeriest': 777,
'swigging': 778,
'furgusson': 779,
'untergang': 780,
'jewelry': 781,
'romanticizing': 782,
'artifacts': 783,
'freebasing': 784,
'brown': 785,
'dareus': 786,
'carltio': 787,
'performaces': 789,
'uncorruptable': 790,
'expressionless': 791,
'meera': 792,
'intermesh': 794,
'searching': 795,
'amuses': 796,
'ization': 797,
'ohio': 798,
'feckless': 799,
'lapyuta': 800,
'endangered': 801,
'grabbers': 802,
'juicy': 803,
'undergarments': 804,
'sartor': 805,
'eko': 806,
'floated': 807,
'cullen': 808,
'demonio': 809,
'dinghy': 810,
'appreciable': 811,
'rafifi': 812,
'personals': 813,
'ravishing': 814,
'classmate': 815,
'tupinambas': 816,
'useless': 817,
'machetes': 818,
'veight': 820,
'regenerate': 821,
'sprinkled': 822,
'darting': 823,
'unnervingly': 825,
'sicken': 826,
'hourglass': 828,
'vierde': 829,
'megazones': 830,
'luthor': 831,
'audacious': 832,
'eggotistical': 833,
'teer': 834,
'prozess': 835,
'scientologists': 836,
'naboombu': 837,
'rocque': 838,
'toi': 839,
'nozires': 840,
'frikkin': 841,
'intended': 842,
'gall': 843,
'operating': 844,
'whoah': 845,
'nashville': 846,
'thank': 847,
'maids': 848,
'moderate': 849,
'novodny': 850,
'molestation': 851,
'lawrence': 852,
'shaker': 853,
'demnio': 854,
'hoshi': 855,
'directly': 856,
'torpedoed': 857,
'reitman': 858,
'humphrey': 859,
'errors': 860,
'mendez': 861,
'sincerity': 862,
'teletubbies': 863,
'comity': 864,
'dachsund': 865,
'farzetta': 866,
'lackey': 867,
'monogamistic': 868,
'ipod': 869,
'quitting': 870,
'prjean': 871,
'boober': 872,
'ogles': 873,
'peculiarities': 874,
'godamnawful': 875,
'gospels': 876,
'customs': 877,
'hooknose': 878,
'eds': 879,
'brontean': 880,
'rou': 881,
'gr': 882,
'dagoba': 883,
'ryall': 884,
'convincing': 885,
'approach': 886,
'marginal': 887,
'birnley': 888,
'stedicam': 889,
'dabbi': 890,
'redecorating': 891,
'feb': 892,
'developmental': 893,
'flagitious': 894,
'anew': 895,
'sprinted': 896,
'raphel': 897,
'retch': 898,
'historicaly': 899,
'lees': 900,
'guerin': 901,
'professions': 902,
'poulain': 903,
'emanated': 904,
'goodnik': 905,
'concerto': 906,
'andrew': 907,
'daffily': 908,
'fireplaces': 909,
'discomfited': 910,
'ballistic': 911,
'thunderously': 912,
'alumnus': 913,
'rodentz': 914,
'briley': 915,
'regarded': 916,
'keymaster': 917,
'haig': 918,
'untied': 919,
'marriage': 920,
'transamerica': 921,
'elektra': 922,
'buchfellner': 923,
'quakerly': 924,
'acquired': 925,
'lobotomized': 926,
'breslin': 927,
'bravora': 928,
'unknowable': 929,
'barbirino': 930,
'makepeace': 931,
'daffy': 932,
'bergmanesque': 933,
'teamsters': 934,
'annick': 935,
'gostoso': 936,
'rakowsky': 937,
'enzo': 938,
'why': 939,
'witted': 940,
'favour': 941,
'scholar': 942,
'reaped': 943,
'pacifier': 944,
'mccombs': 945,
'agent': 946,
'wisecracker': 947,
'superfical': 948,
'mujhe': 949,
'cultured': 950,
'hein': 951,
'norden': 952,
'besiege': 953,
'pelicangs': 954,
'buttress': 955,
'leavitt': 956,
'gu': 957,
'wolfstein': 958,
'brusque': 959,
'pieces': 960,
'uncouth': 961,
'strtebeker': 962,
'interval': 963,
'lustreless': 964,
'greydon': 965,
'plata': 966,
'bhagyashree': 967,
'masiela': 968,
'brandos': 969,
'abo': 970,
'loaner': 971,
'stapled': 972,
'forslani': 973,
'vindicate': 974,
'cripple': 975,
'dem': 976,
'artistical': 977,
'avec': 978,
'hollwyood': 979,
'heist': 980,
'katch': 981,
'atmosphereic': 982,
'zippo': 983,
'disengorges': 984,
'mtro': 985,
'chewie': 986,
'renata': 987,
'technicalities': 988,
'mde': 989,
'gerard': 990,
'renea': 991,
'titillated': 992,
'fictionalized': 993,
'notethe': 994,
'loansharks': 995,
'invigorates': 996,
'payoff': 997,
'kuriyami': 998,
'relentlessy': 999,
...}

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

In [22]:

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 [23]:

layer_0

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

Out[23]:

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

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

In [24]:

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

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

In [25]:

labels[0]

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

Out[25]:

'POSITIVE'

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

In [26]:

get_target_for_label(labels[0])

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

Out[26]:

1

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

In [27]:

labels[1]

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

Out[27]:

'NEGATIVE'

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

In [28]:

get_target_for_label(labels[1])

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

Out[28]:

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

• Re-watch previous week's Udacity Lectures
``````

In [29]:

import time
import sys
import numpy as np

class zSentimentNetwork:
def __init__(self, reviews, labels, hidd_nodes = 10, learning_rate = .1):

# z random generator
np.random.seed(1)

self.pre_process_data(reviews, labels)
self.init_network(len(self.review_vocab), hidd_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

# Start 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 [32]:

mlp = zSentimentNetwork(reviews[:-1000], labels[:-1000], learning_rate=.1)

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

In [35]:

# evaluate model before training
mlp.test(reviews[-1000:], labels[-1000:])

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

Progress:99.9% Speed(reviews/sec):1166.% #Correct:500 #Tested:1000 Testing Accuracy:50.0%

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

In [36]:

# 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):156.0 #Correct:1250 #Trained:2501 Training Accuracy:49.9%
Progress:20.8% Speed(reviews/sec):156.5 #Correct:2500 #Trained:5001 Training Accuracy:49.9%
Progress:21.0% Speed(reviews/sec):156.4 #Correct:2522 #Trained:5045 Training Accuracy:49.9%

---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
1 # train the network
----> 2 mlp.train(reviews[:-1000],labels[:-1000])

<ipython-input-29-14000504f2cb> 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 [40]:

mlp = zSentimentNetwork(reviews[:-1000],labels[:-1000], learning_rate=0.001)
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):154.2 #Correct:1264 #Trained:2501 Training Accuracy:50.5%
Progress:20.8% Speed(reviews/sec):150.6 #Correct:2552 #Trained:5001 Training Accuracy:51.0%
Progress:24.9% Speed(reviews/sec):151.3 #Correct:3098 #Trained:6000 Training Accuracy:51.6%

---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
<ipython-input-40-0c15317bd2ab> in <module>()
1 mlp = zSentimentNetwork(reviews[:-1000],labels[:-1000], learning_rate=0.001)
----> 2 mlp.train(reviews[:-1000],labels[:-1000])

<ipython-input-29-14000504f2cb> 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:

``````
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