# 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 [6]:

from collections import Counter
import numpy as np

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

In [7]:

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

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

In [8]:

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 [9]:

positive_counts.most_common()

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

Out[9]:

[('', 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 [10]:

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 [11]:

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

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

Out[11]:

[('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 [12]:

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

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

Out[12]:

[('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 [13]:

from IPython.display import Image

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

Image(filename='sentiment_network.png')

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

Out[13]:

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

In [14]:

review = "The movie was excellent"

Image(filename='sentiment_network_pos.png')

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

Out[14]:

``````

# Project 2: Creating the Input/Output Data

``````

In [15]:

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

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

74074

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

In [16]:

list(vocab)

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

Out[16]:

['',
'cort',
'technicians',
'nugget',
'kurdish',
'heron',
'egoism',
'enbom',
'thigh',
'shelves',
'tended',
'disable',
'lustreless',
'recurring',
'illuminated',
'prudent',
'romany',
'folksy',
'stapelton',
'pacific',
'insulated',
'facilty',
'inchon',
'idjits',
'harrar',
'myron',
'shity',
'ardala',
'signposting',
'birtwhistle',
'moocow',
'maximally',
'simpson',
'wuss',
'mulrony',
'shuffle',
'mutilated',
'lurches',
'bartley',
'sartorius',
'gogool',
'mcaffee',
'talosian',
'closest',
'tos',
'sanechaos',
'bassis',
'isolating',
'whalin',
'pter',
'dil',
'camp',
'cobblestones',
'girard',
'theosophy',
'malicious',
'vieques',
'crimefighting',
'satanist',
'normalizing',
'shrunken',
'compulsive',
'auntie',
'entrapment',
'jz',
'fazes',
'jha',
'conjunction',
'kolbe',
'yousef',
'gainfully',
'ozric',
'phillipenes',
'salutary',
'ambrose',
'cultism',
'tortilla',
'camui',
'megapack',
'zx',
'idealistic',
'pacts',
'mirroed',
'burning',
'naivet',
'splices',
'hilcox',
'parroting',
'utlimately',
'aaww',
'counselled',
'shillings',
'friendkin',
'increments',
'mcnairy',
'blaylock',
'verbalizations',
'sketch',
'jensen',
'toulon',
'ny',
'pyare',
'retrospect',
'nitu',
'danish',
'serie',
'gasses',
'obscenity',
'bvds',
'tanger',
'lanquage',
'eulogies',
'understated',
'mascara',
'pest',
'mazes',
'bijomaru',
'resolutions',
'bluff',
'stemmed',
'olajima',
'claudio',
'products',
'dungy',
'jeffries',
'reacquainted',
'rodriguez',
'hue',
'shepherds',
'lockjaw',
'ace',
'smiley',
'sleestak',
'recherche',
'moisture',
'bible',
'aptitude',
'orbitting',
'unconsumated',
'kak',
'egoistic',
'johan',
'ustashe',
'wearily',
'exceeds',
'backstreets',
'recites',
'londoner',
'siting',
'rossen',
'laboriously',
'fuelling',
'equalizer',
'doled',
'singe',
'daniell',
'tite',
'arbiter',
'famous',
'bleakest',
'fume',
'pacifying',
'wields',
'misstep',
'pols',
'grierson',
'teasingly',
'disappointingly',
'symmetric',
'hosts',
'jewell',
'sesilia',
'oskorblyonnye',
'marcuse',
'fiona',
'geki',
'irreverant',
'plane',
'overgrown',
'airbag',
'fightfest',
'them',
'nearne',
'mendez',
'lederhosen',
'apocalypto',
'refuses',
'levity',
'markell',
'listing',
'nighwatch',
'thembrians',
'hessians',
'iritf',
'diplomatic',
'mutually',
'chun',
'blag',
'butts',
'electrified',
'petersburg',
'rex',
'sugest',
'notables',
'greyhound',
'stoked',
'hoboken',
'prettiest',
'pulsating',
'marched',
'walmington',
'borrowed',
'caiman',
'civilizations',
'arsehole',
'resettled',
'toffs',
'pakistani',
'rocked',
'bennifer',
'reay',
'lion',
'donnell',
'militaristic',
'minimum',
'yearned',
'noodling',
'cheetos',
'betacam',
'corder',
'all',
'darlian',
'blindpassasjer',
'nutjobseen',
'dishwater',
'okerland',
'supplements',
'mcgee',
'blind',
'vert',
'chainsaws',
'abkani',
'livinston',
'forecaster',
'jewels',
'reputed',
'olen',
'hbc',
'moot',
'assumptions',
'queue',
'arron',
'brusk',
'losses',
'dancers',
'maze',
'singlet',
'tightest',
'forsyte',
'snl',
'department',
'paganism',
'yali',
'oakland',
'ostracization',
'ger',
'protecting',
'rainbows',
'bobbitt',
'hit',
'tank',
'knowingis',
'maguires',
'doggedly',
'planetscapes',
'ferdinandvongalitzien',
'viewpoint',
'befriend',
'frontieres',
'excited',
'suplexing',
'parsing',
'presson',
'necrophiliac',
'ulfsak',
'uglying',
'superpowerman',
'artifices',
'yds',
'plugged',
'waiting',
'apotheosis',
'rabidly',
'verma',
'unfortuneatly',
'solipsism',
'submerges',
'parlaying',
'scud',
'monicas',
'ithe',
'gimmickry',
'crude',
'zionist',
'kostner',
'syd',
'doctoress',
'bensonhurst',
'vxzptdtphwdm',
'marley',
'cattlemen',
'nitpick',
'flagellistic',
'soundgarden',
'stretchy',
'italianness',
'imbues',
'hispanic',
'outlands',
'destroys',
'bloodiest',
'metafiction',
'brigid',
'hangdog',
'gloatingly',
'memorably',
'biggs',
'capita',
'eisenmann',
'babban',
'jace',
'masterstroke',
'holst',
'numbering',
'wannabe',
'gaining',
'clearance',
'message',
'dramatizing',
'fun',
'langford',
'extraneous',
'accost',
'productive',
'spiralling',
'snuck',
'savour',
'poseidon',
'pomegranate',
'airial',
'reaaaaallly',
'mrquez',
'iraquis',
'kwami',
'assaulters',
'stinkers',
'cannibalised',
'galley',
'facinating',
'emptiveness',
'aleya',
'ripple',
'thrill',
'dobie',
'jock',
'although',
'defend',
'conceives',
'unpleasantly',
'hastens',
'reestablish',
'warmth',
'yeshua',
'shapeshifting',
'bierce',
'perp',
'zelig',
'crypts',
'slight',
'delphine',
'characther',
'guiseppe',
'chilled',
'loeb',
'serenity',
'niggling',
'defecated',
'gilley',
'dissociative',
'overwhelmingly',
'coincidence',
'lotta',
'obliterate',
'giuliani',
'counterproductive',
'drewitt',
'rebuke',
'bores',
'moeurs',
'clickety',
'cameroun',
'bestow',
'lili',
'ohtsji',
'garibaldi',
'amassed',
'wwe',
'bas',
'ostracized',
'ranching',
'broke',
'meanspirited',
'rungs',
'income',
'condescend',
'secluded',
'brawled',
'safarova',
'fingerprinting',
'lighten',
'mommas',
'cigliutti',
'helium',
'maro',
'needed',
'blossom',
'exeggcute',
'balding',
'rosenstrae',
'hander',
'bri',
'quirk',
'interspersed',
'porno',
'symptoms',
'subsistence',
'laurdale',
'celeste',
'bogard',
'dotes',
'maternal',
'witticisms',
'catastrophe',
'manichaean',
'cha',
'unfold',
'annoyingly',
'unnaturally',
'chopped',
'falwell',
'installments',
'rejuvenation',
'animals',
'cliffhangin',
'blackmailed',
'elliott',
'robowar',
'icf',
'quayle',
'granger',
'unfolds',
'nothan',
'descendents',
'cumentery',
'cohort',
'precisely',
'phenomenon',
'okiyas',
'befittingly',
'chapelle',
'ricci',
'dent',
'microbiology',
'wagner',
'napoleonic',
'decaf',
'sphincter',
'natashia',
'sneakiness',
'pianist',
'karns',
'punctuation',
'perfectness',
'roshan',
'executioner',
'sasural',
'darian',
'superthunderstingcar',
'motorbikes',
'thuy',
'shriveling',
'outriders',
'dnouement',
'altamont',
'reunion',
'cavegirl',
'carerra',
'pseudocomedies',
'drainage',
'whoah',
'vertido',
'fridrik',
'hk',
'stripe',
'icebox',
'modicum',
'deosnt',
'tattoine',
'vinson',
'lilt',
'hypes',
'hemo',
'nurturer',
'crusoe',
'rectal',
'vacated',
'almodvar',
'stonewashed',
'operate',
'particles',
'dedications',
'geology',
'epilogue',
'clamour',
'creepshow',
'mcdonnel',
'wast',
'embrace',
'demean',
'yiddish',
'priests',
'cushions',
'chaps',
'carico',
'hendrix',
'helicopter',
'loosely',
'sepoys',
'incantations',
'somesuch',
'grumbling',
'estevo',
'eroding',
'barjatyas',
'base',
'fanny',
'boogeman',
'walpurgis',
'meagan',
'blaze',
'tilmac',
'flattered',
'stultified',
'palminterri',
'notwithstanding',
'bubble',
'autocracy',
'anton',
'anja',
'wraparound',
'unprotected',
'unmedicated',
'hoyo',
'rural',
'conceding',
'bleeds',
'sympathy',
'screed',
'ees',
'hensley',
'suggest',
'digressing',
'silliest',
'beleaguered',
'meals',
'ebon',
'flamin',
'francis',
'sloppish',
'matkondar',
'croquet',
'continental',
'bleeping',
'daly',
'unshaven',
'birthmark',
'transposing',
'vandicholai',
'prevarications',
'elefant',
'summers',
'hayle',
'pressberger',
'bushco',
'potboiler',
'secondus',
'burgermister',
'rei',
'prichard',
'thlema',
'afterwards',
'curitz',
'villainy',
'hangs',
'apocalyptically',
'olympic',
'extase',
'incentives',
'decreed',
'nrnberg',
'panhandle',
'slathered',
'purgatori',
'publicize',
'yakitate',
'unredeemably',
'dissemination',
'marilee',
'viennese',
'commonsense',
'editions',
'frikkin',
'requiem',
'mhatre',
'mashall',
'commendations',
'anything',
'commentaries',
'great',
'dramatists',
'bled',
'huntsville',
'generalities',
'messick',
'jigen',
'caminho',
'miller',
'halarity',
'lectured',
'refused',
'inferiors',
'guarontee',
'galligan',
'tallman',
'bucketfuls',
'induces',
'individualism',
'lacing',
'wasim',
'empahsise',
'darkwing',
'thrift',
'frisk',
'table',
'wont',
'helga',
'irrefutable',
'posher',
'conduct',
'beatlemaniac',
'dissabordinate',
'comedically',
'munroe',
'gibney',
'undying',
'circular',
'exemplar',
'contingency',
'projectionist',
'essay',
'chancho',
'succumbs',
'dopplebangers',
'folder',
'editorial',
'cuddles',
'ayacoatl',
'announces',
'munnera',
'warpaint',
'ummmm',
'tingled',
'tony',
'tht',
'rodeos',
'machaty',
'enforces',
'muito',
'chainguns',
'entwine',
'harped',
'flawed',
'interfaith',
'bracket',
'cling',
'xiv',
'arcand',
'brassieres',
'glanse',
'restating',
'candyelise',
'weaving',
'quinn',
'ditto',
'winked',
'moderated',
'contemplate',
'unbelievability',
'detail',
'dunsky',
'glistening',
'grope',
'maru',
'ibrahim',
'martains',
'film',
'who',
'include',
'cuts',
'faced',
'witchhunt',
'archambault',
'shin',
'koontz',
'gungan',
'streetfighters',
'willingham',
'oppressiveness',
'clarifies',
'manuscript',
'nec',
'landscapes',
'dabrova',
'parlors',
'generation',
'actresses',
'steinauer',
'staring',
'uncompelling',
'cattivi',
'endings',
'wooohooo',
'raptus',
'finney',
'roomful',
'japanese',
'six',
'ancillary',
'riiiiiiight',
'martian',
'modernizing',
'gasgoine',
'reptilian',
'acrimonious',
'mushrooms',
'fckin',
'glorious',
'transplant',
'elope',
'dopes',
'misfiring',
'dollys',
'wilkinson',
'doorstop',
'bastedo',
'artimisia',
'prochnow',
'assassino',
'mogul',
'disturbing',
'flaccid',
'meddings',
'blanc',
'thety',
'benefactors',
'automakers',
'alecky',
'indications',
'enunciates',
'slater',
'pallet',
'breastfeeding',
'suriyothai',
'lettieri',
'misbehaving',
'hemlock',
'sequence',
'pharmacy',
'jordi',
'decide',
'dumbly',
'delmar',
'scylla',
'outcome',
'light',
'darwell',
'selves',
'scrambling',
'climbing',
'cinemas',
'afganistan',
'centres',
'walerian',
'implanting',
'heywood',
'sedate',
'bunches',
'ayn',
'councellor',
'brechtian',
'gary',
'coyle',
'swathed',
'bemoans',
'primer',
'hearen',
'caca',
'probibly',
'charming',
'ultramodern',
'thingy',
'rarefied',
'renowned',
'berrisford',
'splashes',
'definaetly',
'father',
'vaugn',
'bodysuit',
'manuals',
'universalised',
'eo',
'mag',
'hyperventilating',
'captivity',
'morvern',
'bods',
'onset',
'greengrass',
'philosophies',
'coyote',
'hots',
'dell',
'ahab',
'diplomacy',
'superegos',
'steeleye',
'runyonesque',
'piercings',
'crewed',
'forceful',
'arched',
'classics',
'insincere',
'unprovoked',
'distinguishing',
'bubbly',
'shara',
'stacie',
'norma',
'nimbus',
'japes',
'stimulates',
'manslaughter',
'effected',
'autobots',
'catacombs',
'mysterio',
'menendez',
'ikey',
'clifford',
'divorces',
'verona',
'cock',
'handing',
'naffness',
'galvanize',
'crout',
'joining',
'bernicio',
'mcnally',
'karyo',
'pretended',
'diners',
'childlish',
'dividing',
'khamini',
'snagged',
'benefit',
'bruce',
'kelemen',
'intercuts',
'scan',
'elicit',
'contaminates',
'zulus',
'withering',
'disregards',
'tonk',
'richandson',
'abortion',
'slower',
'qu',
'stairway',
'fdny',
'implausiblities',
'cu',
'necessary',
'shaking',
'replicator',
'inanity',
'miffed',
'garp',
'sudio',
'texturing',
'cratchitt',
'siege',
'daena',
'waistband',
'towner',
'discernment',
'polka',
'drek',
'worthiness',
'peanut',
'conservationists',
'librarians',
'sexshooter',
'holland',
'nullifying',
'meda',
'strenghtens',
'winnie',
'blonde',
'righteous',
'psmith',
'palavras',
'bluer',
'chiba',
'burrowing',
'reassurance',
'dinosaurus',
'liam',
'sexploitational',
'runnin',
'gijn',
'elams',
'armaments',
'fercryinoutloud',
'oceanography',
'iconoclastic',
'mainsequence',
'picker',
'intellectualized',
'monopolist',
'whorde',
'sticked',
'panegyric',
'damns',
'buries',
'tailed',
'leaping',
'moden',
'flic',
'benny',
'piso',
'sidekicks',
'imps',
'seibert',
'weis',
'puertoricans',
'vilarasau',
'everlovin',
'similar',
'hawker',
...]

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

In [17]:

import numpy as np

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

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

Out[17]:

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

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

In [18]:

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

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

Out[18]:

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

In [19]:

word2index = {}

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

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

Out[19]:

{'': 0,
'cort': 1,
'technicians': 2,
'nugget': 3,
'kurdish': 4,
'heron': 5,
'egoism': 6,
'enbom': 8,
'thigh': 9,
'shelves': 10,
'tended': 11,
'disable': 12,
'lustreless': 13,
'recurring': 14,
'illuminated': 15,
'prudent': 16,
'romany': 17,
'folksy': 18,
'stapelton': 19,
'pacific': 20,
'insulated': 21,
'facilty': 22,
'inchon': 23,
'idjits': 24,
'harrar': 25,
'myron': 26,
'shity': 27,
'ardala': 28,
'signposting': 29,
'birtwhistle': 30,
'moocow': 31,
'maximally': 32,
'simpson': 34,
'wuss': 35,
'mulrony': 36,
'shuffle': 38,
'mutilated': 39,
'lurches': 40,
'bartley': 41,
'sartorius': 42,
'gogool': 43,
'mcaffee': 44,
'talosian': 45,
'closest': 46,
'tos': 47,
'sanechaos': 48,
'bassis': 49,
'isolating': 50,
'whalin': 51,
'pter': 52,
'dil': 53,
'camp': 54,
'cobblestones': 55,
'girard': 57,
'theosophy': 58,
'malicious': 59,
'vieques': 60,
'crimefighting': 61,
'satanist': 62,
'normalizing': 63,
'shrunken': 64,
'compulsive': 65,
'auntie': 66,
'entrapment': 67,
'jz': 68,
'fazes': 69,
'jha': 70,
'conjunction': 71,
'kolbe': 72,
'yousef': 73,
'gainfully': 74,
'ozric': 75,
'phillipenes': 76,
'salutary': 77,
'ambrose': 78,
'cultism': 79,
'tortilla': 80,
'camui': 81,
'megapack': 82,
'zx': 83,
'idealistic': 84,
'pacts': 85,
'mirroed': 86,
'burning': 87,
'naivet': 88,
'splices': 89,
'hilcox': 90,
'parroting': 91,
'utlimately': 92,
'aaww': 93,
'counselled': 94,
'shillings': 95,
'friendkin': 96,
'increments': 97,
'mcnairy': 98,
'blaylock': 99,
'verbalizations': 100,
'sketch': 101,
'jensen': 102,
'toulon': 103,
'ny': 104,
'pyare': 105,
'retrospect': 106,
'nitu': 107,
'danish': 109,
'serie': 110,
'gasses': 111,
'obscenity': 112,
'bvds': 113,
'tanger': 114,
'lanquage': 115,
'eulogies': 116,
'understated': 117,
'mascara': 118,
'pest': 119,
'mazes': 120,
'bijomaru': 121,
'resolutions': 122,
'bluff': 123,
'stemmed': 124,
'olajima': 125,
'claudio': 126,
'products': 127,
'dungy': 128,
'jeffries': 129,
'reacquainted': 130,
'rodriguez': 131,
'hue': 132,
'shepherds': 133,
'lockjaw': 134,
'ace': 135,
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'sleestak': 138,
'recherche': 139,
'moisture': 140,
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'misstep': 170,
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'them': 188,
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'mendez': 190,
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'mcgee': 245,
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'moot': 256,
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'marley': 320,
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'slight': 391,
'delphine': 392,
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'modicum': 521,
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'tattoine': 523,
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'mcdonnel': 541,
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'matkondar': 596,
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'daly': 600,
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'pressberger': 609,
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'potboiler': 611,
'secondus': 612,
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'rei': 614,
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'thlema': 616,
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'miller': 654,
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'munroe': 681,
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'munnera': 697,
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'tingled': 700,
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'tht': 702,
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'machaty': 704,
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'muito': 706,
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'interfaith': 711,
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'xiv': 714,
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'restating': 718,
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'ditto': 722,
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'moderated': 724,
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'mogul': 787,
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'alecky': 795,
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'suriyothai': 801,
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'misbehaving': 803,
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'holland': 951,
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'meda': 953,
'strenghtens': 954,
'winnie': 955,
'blonde': 956,
'righteous': 957,
'psmith': 958,
'palavras': 959,
'bluer': 961,
'chiba': 962,
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'reassurance': 964,
'dinosaurus': 965,
'liam': 966,
'sexploitational': 967,
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'fercryinoutloud': 972,
'oceanography': 973,
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'mainsequence': 975,
'picker': 976,
'intellectualized': 977,
'monopolist': 978,
'whorde': 979,
'sticked': 980,
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'damns': 982,
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'tailed': 984,
'leaping': 985,
'moden': 986,
'flic': 987,
'benny': 988,
'piso': 989,
'sidekicks': 990,
'imps': 991,
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'puertoricans': 994,
'vilarasau': 996,
'everlovin': 997,
'similar': 998,
'hawker': 999,
...}

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

In [20]:

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 [21]:

layer_0

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

Out[21]:

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

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

In [22]:

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

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

In [23]:

labels[0]

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

Out[23]:

'POSITIVE'

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

In [24]:

get_target_for_label(labels[0])

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

Out[24]:

1

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

In [25]:

labels[1]

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

Out[25]:

'NEGATIVE'

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

In [26]:

get_target_for_label(labels[1])

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

Out[26]:

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 [27]:

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 [28]:

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

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

In [29]:

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

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

Progress:99.9% Speed(reviews/sec):567.7% #Correct:500 #Tested:1000 Testing Accuracy:50.0%

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

In [30]:

# 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):98.25 #Correct:1250 #Trained:2501 Training Accuracy:49.9%
Progress:20.8% Speed(reviews/sec):93.18 #Correct:2500 #Trained:5001 Training Accuracy:49.9%
Progress:31.2% Speed(reviews/sec):95.35 #Correct:3750 #Trained:7501 Training Accuracy:49.9%
Progress:41.6% Speed(reviews/sec):96.91 #Correct:5000 #Trained:10001 Training Accuracy:49.9%
Progress:52.0% Speed(reviews/sec):97.80 #Correct:6250 #Trained:12501 Training Accuracy:49.9%
Progress:62.5% Speed(reviews/sec):98.47 #Correct:7500 #Trained:15001 Training Accuracy:49.9%
Progress:72.9% Speed(reviews/sec):98.80 #Correct:8750 #Trained:17501 Training Accuracy:49.9%
Progress:83.3% Speed(reviews/sec):98.58 #Correct:10000 #Trained:20001 Training Accuracy:49.9%
Progress:93.7% Speed(reviews/sec):98.85 #Correct:11250 #Trained:22501 Training Accuracy:49.9%
Progress:99.9% Speed(reviews/sec):98.91 #Correct:11999 #Trained:24000 Training Accuracy:49.9%

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

In [31]:

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

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

In [32]:

# 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):142.7 #Correct:1247 #Trained:2501 Training Accuracy:49.8%
Progress:20.8% Speed(reviews/sec):148.8 #Correct:2497 #Trained:5001 Training Accuracy:49.9%
Progress:31.2% Speed(reviews/sec):152.3 #Correct:3747 #Trained:7501 Training Accuracy:49.9%
Progress:41.6% Speed(reviews/sec):153.8 #Correct:4997 #Trained:10001 Training Accuracy:49.9%
Progress:52.0% Speed(reviews/sec):155.0 #Correct:6247 #Trained:12501 Training Accuracy:49.9%
Progress:62.5% Speed(reviews/sec):153.8 #Correct:7485 #Trained:15001 Training Accuracy:49.8%
Progress:72.9% Speed(reviews/sec):154.6 #Correct:8735 #Trained:17501 Training Accuracy:49.9%
Progress:83.3% Speed(reviews/sec):155.0 #Correct:9984 #Trained:20001 Training Accuracy:49.9%
Progress:93.7% Speed(reviews/sec):155.5 #Correct:11234 #Trained:22501 Training Accuracy:49.9%
Progress:99.9% Speed(reviews/sec):155.7 #Correct:11983 #Trained:24000 Training Accuracy:49.9%

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

In [33]:

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

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

In [34]:

# 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):99.23 #Correct:1247 #Trained:2501 Training Accuracy:49.8%
Progress:20.8% Speed(reviews/sec):100.0 #Correct:2535 #Trained:5001 Training Accuracy:50.6%
Progress:31.2% Speed(reviews/sec):100.3 #Correct:3895 #Trained:7501 Training Accuracy:51.9%
Progress:41.6% Speed(reviews/sec):100.5 #Correct:5398 #Trained:10001 Training Accuracy:53.9%
Progress:52.0% Speed(reviews/sec):100.6 #Correct:6933 #Trained:12501 Training Accuracy:55.4%
Progress:62.5% Speed(reviews/sec):100.5 #Correct:8512 #Trained:15001 Training Accuracy:56.7%
Progress:72.9% Speed(reviews/sec):100.6 #Correct:10118 #Trained:17501 Training Accuracy:57.8%
Progress:83.3% Speed(reviews/sec):100.6 #Correct:11767 #Trained:20001 Training Accuracy:58.8%
Progress:93.7% Speed(reviews/sec):100.6 #Correct:13500 #Trained:22501 Training Accuracy:59.9%
Progress:99.9% Speed(reviews/sec):100.6 #Correct:14545 #Trained:24000 Training Accuracy:60.6%

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

In [ ]:

``````