In [2]:
# import package
# import the clean_dataset
import pandas as pd
remarks = pd.read_csv(‘AIT590_Progress_Report_CleanedDataset_Team4_Jian_Benxi_Chloe_Pei.csv’)
df = pd.DataFrame(remarks)
# check the dataset
df
Out[2]:
Unnamed: 0
class
tweet
Label
clean_tweet
tokens
0
0
2
!!! RT @mayasolovely: As a woman you shouldn’t…
Neutral
woman complain clean hous man alway take trash
[‘woman’, ‘complain’, ‘clean’, ‘hous’, ‘man’, …
1
40
2
” momma said no pussy cats inside my doghouse ”
Neutral
momma said pussi cat insid doghous
[‘momma’, ‘said’, ‘pussi’, ‘cat’, ‘insid’, ‘do…
2
63
2
“@Addicted2Guys: -SimplyAddictedToGuys http://…
Neutral
simplyaddictedtoguy woof woof hot scalli lad
[‘simplyaddictedtoguy’, ‘woof’, ‘woof’, ‘hot’,…
3
66
2
“@AllAboutManFeet: http://t.co/3gzUpfuMev” woo…
Neutral
woof woof hot sole
[‘woof’, ‘woof’, ‘hot’, ‘sole’]
4
67
2
“@Allyhaaaaa: Lemmie eat a Oreo & do these…
Neutral
lemmi eat oreo dish one oreo laugh loud
[‘lemmi’, ‘eat’, ‘oreo’, ‘dish’, ‘one’, ‘oreo’…
…
…
…
…
…
…
…
5588
24767
2
you know what they say, the early bird gets th…
Neutral
know say earli bird worm put gummi worm morn c…
[‘know’, ‘say’, ‘earli’, ‘bird’, ‘worm’, ‘put’…
5589
24776
0
you’re all niggers
Negative
nigger
[‘nigger’]
5590
24777
0
you’re such a retard i hope you get type 2 dia…
Negative
retard hope type diabet die sugar rush fuck fa…
[‘retard’, ‘hope’, ‘type’, ‘diabet’, ‘die’, ‘s…
5591
24779
2
you’ve gone and broke the wrong heart baby, an…
Neutral
gone broke wrong heart babi drove redneck crazi
[‘gone’, ‘broke’, ‘wrong’, ‘heart’, ‘babi’, ‘d…
5592
24782
2
~~Ruffled | Ntac Eileen Dahlia – Beautiful col…
Neutral
ruffl ntac eileen dahlia beauti color combin p…
[‘ruffl’, ‘ntac’, ‘eileen’, ‘dahlia’, ‘beauti’…
5593 rows × 6 columns
In [3]:
# create a new column “Sentiment” to filter hate speech and non-hate speech by using column “label”
# 1 stands for hate speech and 0 stands for non-hate speech
df[‘Sentiment’] = df[‘Label’].replace([“Negative”,”Neutral”],[1,0])
# check the dataset
df
Out[3]:
Unnamed: 0
class
tweet
Label
clean_tweet
tokens
Sentiment
0
0
2
!!! RT @mayasolovely: As a woman you shouldn’t…
Neutral
woman complain clean hous man alway take trash
[‘woman’, ‘complain’, ‘clean’, ‘hous’, ‘man’, …
0
1
40
2
” momma said no pussy cats inside my doghouse ”
Neutral
momma said pussi cat insid doghous
[‘momma’, ‘said’, ‘pussi’, ‘cat’, ‘insid’, ‘do…
0
2
63
2
“@Addicted2Guys: -SimplyAddictedToGuys http://…
Neutral
simplyaddictedtoguy woof woof hot scalli lad
[‘simplyaddictedtoguy’, ‘woof’, ‘woof’, ‘hot’,…
0
3
66
2
“@AllAboutManFeet: http://t.co/3gzUpfuMev” woo…
Neutral
woof woof hot sole
[‘woof’, ‘woof’, ‘hot’, ‘sole’]
0
4
67
2
“@Allyhaaaaa: Lemmie eat a Oreo & do these…
Neutral
lemmi eat oreo dish one oreo laugh loud
[‘lemmi’, ‘eat’, ‘oreo’, ‘dish’, ‘one’, ‘oreo’…
0
…
…
…
…
…
…
…
…
5588
24767
2
you know what they say, the early bird gets th…
Neutral
know say earli bird worm put gummi worm morn c…
[‘know’, ‘say’, ‘earli’, ‘bird’, ‘worm’, ‘put’…
0
5589
24776
0
you’re all niggers
Negative
nigger
[‘nigger’]
1
5590
24777
0
you’re such a retard i hope you get type 2 dia…
Negative
retard hope type diabet die sugar rush fuck fa…
[‘retard’, ‘hope’, ‘type’, ‘diabet’, ‘die’, ‘s…
1
5591
24779
2
you’ve gone and broke the wrong heart baby, an…
Neutral
gone broke wrong heart babi drove redneck crazi
[‘gone’, ‘broke’, ‘wrong’, ‘heart’, ‘babi’, ‘d…
0
5592
24782
2
~~Ruffled | Ntac Eileen Dahlia – Beautiful col…
Neutral
ruffl ntac eileen dahlia beauti color combin p…
[‘ruffl’, ‘ntac’, ‘eileen’, ‘dahlia’, ‘beauti’…
0
5593 rows × 7 columns
In [4]:
# count the values of “sentiment”
df.Sentiment.value_counts()
Out[4]:
0 4163
1 1430
Name: Sentiment, dtype: int64
In [5]:
# feature generation
# word embeeding method 1:Bag of Words
from sklearn.feature_extraction.text import CountVectorizer
import numpy as np
cv = CountVectorizer()
x = cv.fit_transform(df[‘tokens’])
text_counts = x
In [6]:
cv.vocabulary_ # show resulting vocabulary; the numbers are not counts, they are the position in the sparse vector.
Out[6]:
{‘woman’: 8023,
‘complain’: 1506,
‘clean’: 1373,
‘hous’: 3415,
‘man’: 4335,
‘alway’: 190,
‘take’: 7054,
‘trash’: 7389,
‘momma’: 4641,
‘said’: 6145,
‘pussi’: 5689,
‘cat’: 1161,
‘insid’: 3596,
‘doghous’: 2058,
‘simplyaddictedtoguy’: 6477,
‘woof’: 8033,
‘hot’: 3410,
‘scalli’: 6208,
‘lad’: 3974,
‘sole’: 6645,
‘lemmi’: 4083,
‘eat’: 2209,
‘oreo’: 5103,
‘dish’: 2020,
‘one’: 5066,
‘laugh’: 4019,
‘loud’: 4242,
‘eggplant’: 2239,
’emoji’: 2286,
‘doe’: 2054,
‘say’: 6203,
‘look’: 4221,
‘scream’: 6247,
‘lmao’: 4180,
‘babi’: 460,
‘monkey’: 4652,
‘bathtim’: 563,
‘awwww’: 446,
‘soooo’: 6669,
‘ador’: 76,
‘queer’: 5703,
‘gaywad’: 2858,
‘alsarabsss’: 180,
‘beaner’: 587,
‘smh’: 6584,
‘tell’: 7138,
‘mexican’: 4519,
‘fuck’: 2770,
‘gay’: 2855,
‘blacklist’: 743,
‘hoe’: 3326,
‘hold’: 3332,
‘tehgodclan’: 7133,
‘anyway’: 275,
‘basebal’: 549,
‘season’: 6266,
‘win’: 7985,
‘yanke’: 8103,
‘love’: 4247,
‘start’: 6815,
‘earli’: 2198,
‘bird’: 715,
‘night’: 4909,
‘owl’: 5162,
‘wise’: 8002,
‘worm’: 8050,
‘overdos’: 5148,
‘heavi’: 3229,
‘drug’: 2147,
‘sound’: 6690,
‘bad’: 471,
‘tonight’: 7321,
‘shit’: 6398,
‘everi’: 2372,
‘day’: 1820,
‘pit’: 5403,
‘bull’: 990,
‘photograph’: 5361,
‘fairi’: 2443,
‘tale’: 7058,
‘creatur’: 1680,
‘long’: 4214,
‘laker’: 3985,
‘could’: 1624,
‘careless’: 1130,
‘real’: 5815,
‘hahaha’: 3126,
‘play’: 5425,
‘soccer’: 6625,
‘cheat’: 1253,
‘girl’: 2918,
‘wear’: 7855,
‘khaki’: 3880,
‘colour’: 1467,
‘cargo’: 1132,
‘haircut’: 3135,
‘yup’: 8168,
‘ghetto’: 2903,
‘smart’: 6575,
‘idc’: 3477,
‘want’: 7803,
‘nose’: 4961,
‘boi’: 807,
‘chav’: 1245,
‘sockfetish’: 6631,
‘stink’: 6865,
‘sox’: 6703,
‘leg’: 4070,
‘peel’: 5288,
‘bring’: 924,
‘back’: 462,
‘rewind’: 5988,
‘move’: 4707,
‘shaq’: 6360,
‘line’: 4151,
‘ooooow’: 5077,
‘ucfpinkparti’: 7513,
‘come’: 1476,
‘stay’: 6828,
‘aliv’: 153,
‘knight’: 3930,
‘nation’: 4801,
‘still’: 6862,
‘special’: 6720,
‘comeatmeut’: 1477,
‘tune’: 7460,
‘updat’: 7608,
‘report’: 5934,
‘week’: 7872,
‘bum’: 996,
‘lmfao’: 4188,
‘lmfaoooo’: 4190,
‘hate’: 3198,
‘black’: 740,
‘peopl’: 5301,
‘nigger’: 4902,
‘piss’: 5401,
‘past’: 5243,
‘would’: 8058,
‘lick’: 4122,
‘dirti’: 2006,
‘slept’: 6549,
‘frat’: 2722,
‘boy’: 867,
‘soft’: 6636,
‘sexi’: 6337,
‘arch’: 311,
‘hick’: 3282,
‘grandkid’: 3025,
‘may’: 4422,
‘never’: 4862,
‘see’: 6280,
‘thank’: 7167,
‘climat’: 1388,
‘chang’: 1219,
‘least’: 4050,
‘ariza’: 322,
‘snake’: 6599,
‘coward’: 1646,
‘crippl’: 1697,
‘hero’: 3268,
‘roach’: 6035,
‘lmaoo’: 4181,
‘foster’: 2697,
‘home’: 3347,
‘imaginari’: 3506,
‘whoa’: 7949,
‘chill’: 1284,
‘show’: 6431,
‘everyth’: 2378,
‘welfar’: 7890,
‘govern’: 3008,
‘aid’: 120,
‘claim’: 1357,
‘white’: 7936,
‘slander’: 6537,
‘drake’: 2114,
‘new’: 4867,
‘shoe’: 6410,
‘releas’: 5905,
‘nike’: 4912,
‘jordan’: 3799,
‘glitter’: 2938,
‘dude’: 2162,
‘fag’: 2433,
‘inna’: 3588,
‘alon’: 172,
‘austin’: 415,
‘teach’: 7110,
‘curv’: 1754,
‘know’: 3936,
‘eli’: 2262,
‘threw’: 7228,
‘nfl’: 4879,
‘lead’: 4041,
‘intercept’: 3616,
‘usc’: 7629,
‘senior’: 6304,
‘anthoni’: 265,
‘brown’: 953,
‘call’: 1074,
‘head’: 3214,
‘coach’: 1417,
‘steve’: 6853,
‘sarkisian’: 6180,
‘racist’: 5725,
‘quit’: 5715,
‘team’: 7112,
‘bitch’: 727,
‘tuku’: 7455,
‘idk’: 3486,
‘blah’: 747,
‘mad’: 4298,
‘cuh’: 1735,
‘suur’: 7001,
‘ita’: 3670,
‘gerryshalloweenparti’: 2892,
‘halloween’: 3148,
‘yesterday’: 8128,
‘stupid’: 6934,
‘sad’: 6139,
‘charact’: 1230,
‘death’: 1834,
‘bitter’: 732,
‘stark’: 6814,
‘smuggler’: 6596,
‘explain’: 2406,
‘help’: 3257,
‘fighter’: 2547,
‘along’: 173,
‘jihadi’: 3770,
‘highway’: 3293,
‘ralli’: 5751,
‘bottom’: 853,
‘rallycap’: 5752,
‘bore’: 839,
‘suppos’: 6984,
‘cute’: 1760,
‘faggot’: 2435,
‘sometim’: 6658,
‘anoth’: 261,
‘treasur’: 7404,
‘lassen’: 4006,
‘feel’: 2517,
‘youll’: 8147,
‘nig’: 4894,
‘doubt’: 2094,
‘ashley’: 354,
‘young’: 8149,
‘tri’: 7414,
‘deni’: 1899,
‘land’: 3993,
‘mouth’: 4703,
‘ride’: 6004,
‘sit’: 6500,
‘window’: 7988,
‘sill’: 6467,
‘open’: 5079,
‘fli’: 2621,
‘smack’: 6572,
‘protecttheanim’: 5633,
‘video’: 7702,
‘entir’: 2317,
‘medium’: 4459,
‘mock’: 4622,
‘crybabi’: 1723,
‘patron’: 5255,
‘hajjaj’: 3142,
‘ajmi’: 128,
‘arrest’: 333,
‘kuwait’: 3965,
‘airport’: 127,
‘tweeti’: 7485,
‘tho’: 7213,
‘twitter’: 7497,
‘rapper’: 5780,
‘link’: 4158,
‘jacki’: 3692,
‘retard’: 5970,
‘blondeproblem’: 784,
‘make’: 4319,
‘grill’: 3061,
‘chees’: 1262,
‘photo’: 5359,
‘heaven’: 3227,
‘stacey’: 6790,
‘dash’: 1810,
‘baddest’: 474,
‘evaaaa’: 2363,
‘actin’: 59,
‘spook’: 6759,
‘color’: 1465,
‘contact’: 1566,
‘eye’: 2421,
‘blinder’: 771,
‘mine’: 4578,
‘caus’: 1172,
‘swear’: 7010,
‘easili’: 2206,
‘behind’: 618,
‘nip’: 4917,
‘well’: 7891,
‘charli’: 1238,
‘chaplin’: 1226,
‘histori’: 3313,
‘photographi’: 5362,
‘told’: 7308,
‘mariam’: 4371,
‘cigarett’: 1338,
‘birthday’: 722,
‘also’: 181,
‘browni’: 955,
‘kind’: 3905,
‘fit’: 2586,
‘nice’: 4882,
‘gear’: 2864,
‘trainer’: 7372,
‘saw’: 6199,
‘ajumma’: 129,
‘walk’: 7791,
‘begin’: 613,
‘wow’: 8063,
‘even’: 2367,
‘listen’: 4164,
‘rememb’: 5919,
‘wop’: 8037,
‘god’: 2957,
‘find’: 2562,
‘rawr’: 5802,
‘french’: 2736,
‘frenchscal’: 2738,
‘albino’: 137,
‘bae’: 479,
‘word’: 8039,
‘use’: 7630,
‘someth’: 6655,
‘movi’: 4710,
‘chine’: 1292,
‘maverick’: 4417,
‘commun’: 1498,
‘comrad’: 1515,
‘give’: 2927,
‘imperialist’: 3522,
‘yank’: 8102,
‘birdi’: 719,
‘worri’: 8051,
‘nigga’: 4899,
‘dat’: 1811,
‘fuckin’: 2776,
‘gener’: 2876,
‘ascii’: 348,
‘box’: 865,
‘nodej’: 4930,
‘abil’: 14,
‘flappi’: 2605,
‘node’: 4929,
‘via’: 7691,
‘tube’: 7453,
‘hey’: 3279,
‘found’: 2702,
‘kidnap’: 3892,
‘ohio’: 5041,
‘shitmybosssay’: 6400,
‘draft’: 2110,
‘gung’: 3099,
‘folk’: 2659,
‘send’: 6303,
‘afghanistan’: 92,
‘war’: 7806,
‘zone’: 8204,
‘thrill’: 7230,
‘billi’: 707,
‘hillbilli’: 3301,
‘heart’: 3224,
‘hahahahaha’: 3129,
‘jesu’: 3754,
‘murphi’: 4743,
‘way’: 7841,
‘chug’: 1327,
‘alot’: 175,
‘shitallysay’: 6399,
‘slit’: 6560,
‘throat’: 7233,
‘neck’: 4826,
‘dad’: 1768,
‘asian’: 357,
‘follow’: 2660,
‘yellow’: 8123,
‘brick’: 912,
‘wendi’: 7896,
‘davi’: 1815,
‘scientist’: 6233,
‘creat’: 1676,
‘remot’: 5922,
‘control’: 1579,
‘chick’: 1277,
‘studi’: 6924,
‘shi’: 6389,
‘penguin’: 5294,
‘let’: 4097,
‘kill’: 3898,
‘cracker’: 1657,
‘hear’: 3222,
‘mam’: 4332,
‘ren’: 5924,
‘doet’: 2056,
‘haar’: 3119,
‘rol’: 6059,
‘net’: 4856,
‘ongesteld’: 5068,
‘wat’: 7825,
‘van’: 7655,
‘soort’: 6672,
‘vragen’: 7763,
‘waar’: 7770,
‘denkt’: 1902,
‘die’: 1970,
‘weer’: 7877,
‘uit’: 7527,
‘dmx’: 2045,
‘kirk’: 3914,
‘franklin’: 2720,
‘neighbor’: 4835,
‘beat’: 594,
‘kid’: 3890,
‘grandma’: 3026,
‘porch’: 5494,
‘time’: 7271,
‘think’: 7204,
‘refer’: 5871,
‘broken’: 938,
‘bottl’: 852,
‘knife’: 3929,
‘nah’: 4775,
‘thought’: 7223,
‘photoshop’: 5363,
‘face’: 2427,
‘smmfh’: 6588,
‘yall’: 8099,
‘ovo’: 5159,
‘kendrick’: 3864,
‘lamar’: 3987,
‘coupl’: 1636,
‘red’: 5853,
‘gotten’: 3005,
‘first’: 2579,
‘base’: 547,
‘mayb’: 4425,
‘need’: 4830,
‘game’: 2828,
‘aryan’: 344,
‘race’: 5719,
‘stand’: 6802,
‘mongerl’: 4650,
‘turn’: 7467,
‘world’: 8046,
‘slum’: 6567,
‘devil’: 1945,
‘grow’: 3073,
‘selfish’: 6295,
‘wick’: 7965,
‘restrict’: 5965,
‘water’: 7829,
‘healthi’: 3221,
‘student’: 6923,
‘sour’: 6693,
‘appl’: 292,
‘wit’: 8006,
‘divis’: 2040,
‘saint’: 6146,
‘gon’: 2972,
‘nfc’: 4878,
‘south’: 6695,
‘watch’: 7826,
‘recip’: 5840,
‘leftov’: 4069,
‘hell’: 3246,
‘eaten’: 2210,
‘yet’: 8130,
‘two’: 7499,
‘road’: 6036,
‘diverg’: 2038,
‘wood’: 8030,
‘chose’: 1312,
‘tunheimr’: 7461,
‘robert’: 6042,
‘frost’: 2759,
‘giant’: 2904,
‘spic’: 6737,
‘kkk’: 3923,
‘hut’: 3460,
‘butt’: 1035,
‘callin’: 1078,
‘jiggaboo’: 3768,
‘marriag’: 4384,
‘okay’: 5045,
‘beyond’: 679,
‘sorri’: 6679,
‘futur’: 2801,
‘son’: 6660,
‘street’: 6903,
‘hand’: 3157,
‘yah’: 8095,
‘mtzra’: 4719,
‘egyptian’: 2242,
‘ysrah’: 8161,
‘inherit’: 3578,
‘kushit’: 3963,
‘ethiopian’: 2357,
‘yaoooooooo’: 8106,
‘mawnin’: 4420,
‘yardi’: 8109,
‘gunna’: 3100,
‘spear’: 6718,
‘chucker’: 1325,
‘hit’: 3314,
‘light’: 4136,
‘toco’: 7296,
‘bell’: 631,
‘morn’: 4674,
‘diana’: 1960,
‘miracl’: 4588,
‘littl’: 4169,
‘dress’: 2126,
‘trout’: 7437,
‘deeeeaaaadd’: 1852,
‘teeth’: 7130,
‘star’: 6810,
‘aww’: 444,
‘yeah’: 8117,
‘far’: 2471,
‘away’: 438,
‘ayo’: 452,
‘handicap’: 3161,
‘scalp’: 6209,
‘close’: 1398,
‘tripl’: 7426,
‘six’: 6506,
‘hater’: 3199,
‘suck’: 6951,
‘dick’: 1964,
‘quick’: 5707,
‘someon’: 6654,
‘youtub’: 8155,
‘comment’: 1490,
‘good’: 2977,
‘friend’: 2747,
‘raver’: 5799,
‘venn’: 7674,
‘diagram’: 1956,
‘larg’: 4001,
‘intersect’: 3624,
‘name’: 4785,
‘lord’: 4231,
‘poor’: 5484,
‘whitey’: 7945,
‘twat’: 7479,
‘weird’: 7885,
‘fave’: 2497,
‘snoop’: 6612,
‘justin’: 3825,
‘much’: 4721,
‘fun’: 2787,
‘wilson’: 7983,
‘tribut’: 7416,
‘betaward’: 667,
‘azmonsoon’: 455,
‘lot’: 4238,
‘rain’: 5746,
‘enough’: 2310,
‘wash’: 7815,
‘teabagg’: 7109,
‘state’: 6822,
‘tcot’: 7103,
‘teaparti’: 7116,
‘azflood’: 454,
‘askdem’: 363,
’embarrass’: 2277,
‘rangel’: 5772,
‘remain’: 5914,
‘caucu’: 1169,
‘break’: 897,
‘mass’: 4398,
‘governor’: 3009,
‘baker’: 491,
‘live’: 4173,
‘coverag’: 1642,
‘wcvb’: 7847,
‘stream’: 6902,
‘onlin’: 5069,
‘bigbird’: 691,
‘made’: 4300,
‘gop’: 2988,
‘exist’: 2398,
‘sinc’: 6482,
‘creator’: 1679,
‘jail’: 3703,
‘sodomi’: 6634,
‘blessjesu’: 766,
‘amo’: 214,
‘togeth’: 7302,
‘except’: 2392,
‘agre’: 110,
‘uphold’: 7611,
‘justic’: 3823,
‘court’: 1638,
‘detest’: 1938,
‘truth’: 7445,
‘crown’: 1710,
‘foot’: 2667,
‘blessjesusair’: 767,
‘matthew’: 4412,
‘air’: 124,
‘heavenli’: 3228,
‘father’: 2488,
‘feed’: 2515,
‘valuabl’: 7653,
‘blessjesuslov’: 768,
‘happi’: 3174,
‘belat’: 626,
‘sixti’: 6509,
‘fifth’: 2545,
‘aunt’: 412,
‘mari’: 4368,
‘agn’: 107,
‘strong’: 6914,
‘california’: 1073,
‘full’: 2785,
‘oklahoma’: 5049,
‘commonquestioniget’: 1497,
‘uou’: 7607,
‘dtla’: 2155,
‘non’: 4941,
‘european’: 2359,
‘allow’: 164,
‘degenerateartist’: 1875,
‘sniff’: 6608,
‘whiffi’: 7927,
‘ball’: 499,
‘involuntari’: 3638,
‘cuz’: 1764,
‘downtown’: 2108,
‘hairi’: 3136,
‘climatechang’: 1389,
‘scari’: 6214,
‘sen’: 6301,
‘sneez’: 6607,
‘derekjet’: 1917,
‘lifelong’: 4131,
‘miss’: 4595,
‘firstballot’: 2580,
‘unanim’: 7537,
‘dutch’: 2187,
‘farmer’: 2477,
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‘flu’: 2644,
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‘futurist’: 2802,
‘old’: 5050,
‘earth’: 2203,
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‘wors’: 8052,
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‘convinc’: 1582,
‘firecashman’: 2574,
‘root’: 6077,
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‘octob’: 5024,
‘glori’: 2941,
‘isaiah’: 3655,
‘mom’: 4638,
‘ask’: 361,
‘fast’: 2483,
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‘glizzi’: 2939,
‘feat’: 2506,
‘pli’: 5436,
‘free’: 2729,
‘tha’: 7165,
‘gang’: 2832,
‘happycolumbusday’: 3178,
‘america’: 204,
‘karlmarx’: 3843,
‘share’: 6363,
‘coloni’: 1464,
‘injun’: 3580,
‘peta’: 5333,
‘callou’: 1079,
‘buffalo’: 979,
‘hellyeah’: 3254,
‘fall’: 2448,
‘pumpkin’: 5667,
‘spice’: 6738,
‘hellobrookland’: 3252,
‘serv’: 6319,
‘sampl’: 6160,
‘cider’: 1336,
‘vegan’: 7667,
‘slider’: 6552,
‘holyspirit’: 3345,
‘joshua’: 3803,
‘place’: 5413,
‘shall’: 6353,
‘tread’: 7401,
‘upon’: 7613,
‘given’: 2928,
‘unto’: 7603,
‘mose’: 4683,
‘straight’: 6894,
‘colleg’: 1458,
‘champion’: 1213,
‘hoosiern’: 3389,
‘hoosier’: 3386,
‘honorari’: 3376,
‘cooler’: 1591,
‘intern’: 3621,
‘footbal’: 2668,
‘card’: 1125,
‘indigenouspeoplesday’: 3550,
‘right’: 6011,
‘redskin’: 5862,
‘queen’: 5701,
‘nomg’: 4938,
‘sofunni’: 6638,
‘iowa’: 3642,
‘iraq’: 3647,
‘gov’: 3007,
‘troop’: 7431,
‘sever’: 6332,
‘defeat’: 1862,
‘isi’: 3656,
‘saqlawiya’: 6175,
‘chatter’: 1244,
‘soldier’: 6644,
‘lost’: 4237,
‘rave’: 5797,
‘jesuschrist’: 3755,
‘period’: 5315,
‘sonofgod’: 6665,
‘minnesota’: 4581,
‘centuri’: 1196,
‘fob’: 2650,
‘natitud’: 4803,
‘comb’: 1472,
‘yer’: 8126,
‘beard’: 591,
‘wigger’: 7974,
‘nowplay’: 4978,
‘torus’: 7338,
‘blue’: 793,
‘sky’: 6528,
‘radio’: 5732,
‘edit’: 2226,
‘thearrow’: 7171,
‘bitten’: 731,
‘twice’: 7489,
‘ian’: 3467,
‘hunter’: 3448,
‘door’: 2085,
‘wasp’: 7821,
‘texa’: 7159,
‘big’: 690,
‘pdplive’: 5270,
‘novemb’: 4975,
‘ticket’: 7253,
‘pacern’: 5171,
‘perfect’: 5310,
‘continu’: 1574,
‘iubb’: 3680,
‘finish’: 2568,
‘nba’: 4812,
‘pacer’: 5170,
‘indiana’: 3548,
‘radiofreenorthwest’: 5733,
‘covington’: 1644,
‘read’: 5812,
‘respons’: 5959,
‘fbi’: 2504,
‘andi’: 236,
‘polit’: 5462,
‘liv’: 4172,
‘awaken’: 435,
‘gretchen’: 3053,
‘reasonswecantbetogeth’: 5825,
‘talkin’: 7064,
‘smash’: 6578,
‘homi’: 3360,
‘rebelsci’: 5830,
‘actual’: 65,
‘genet’: 2878,
‘engin’: 2304,
‘lab’: 3967,
‘natur’: 4807,
‘clone’: 1397,
‘rome’: 6064,
‘redsox’: 5865,
‘focu’: 2651,
‘oriol’: 5111,
‘knock’: 3933,
‘last’: 4007,
‘year’: 8118,
‘hatr’: 3201,
‘teen’: 7127,
‘fantast’: 2469,
‘readi’: 5813,
‘tomorrow’: 7315,
‘sweep’: 7015,
‘dispos’: 2026,
‘ever’: 2370,
‘month’: 4660,
‘milwauke’: 4571,
‘yost’: 8143,
‘wish’: 8003,
‘guy’: 3109,
‘lose’: 4234,
‘fire’: 2572,
‘girardi’: 2917,
‘ruinacomedian’: 6111,
‘lapdanc’: 3999,
‘skate’: 6512,
‘slightlyadjust’: 6555,
‘all’: 154,
‘wan’: 7800,
‘tweet’: 7483,
‘drink’: 2131,
‘pineappl’: 5391,
‘soda’: 6632,
‘somethingigetalot’: 6657,
‘hawaiian’: 3207,
‘drunk’: 2149,
‘southcarolina’: 6696,
‘stopwhitepeopl’: 6885,
‘case’: 1152,
‘west’: 7902,
‘nile’: 4913,
‘viru’: 7730,
‘infect’: 3563,
‘orang’: 5095,
‘counti’: 1631,
‘dead’: 1826,
‘carri’: 1144,
‘tbt’: 7100,
‘forev’: 2677,
‘ling’: 4154,
‘theboondock’: 7175,
‘nicca’: 4881,
‘david’: 1816,
‘scandal’: 6211,
‘thingsiwillteachmychild’: 7202,
‘deez’: 1860,
‘trayvonmartin’: 7400,
‘zimmerman’: 8193,
‘creepi’: 1687,
‘thug’: 7240,
‘tweetlikepontiacholm’: 7486,
‘pontiac’: 5478,
‘sprinkler’: 6770,
‘vinitahegwood’: 7714,
‘job’: 3776,
‘naacp’: 4770,
‘diver’: 2037,
‘toler’: 7309,
‘virginia’: 7726,
‘wearerepublicnotregim’: 7857,
‘potu’: 5520,
‘violat’: 7722,
‘usa’: 7627,
‘sovereignti’: 6702,
‘border’: 838,
‘crimin’: 1695,
‘diseas’: 2017,
‘haitian’: 3139,
‘american’: 205,
‘impeachobama’: 3518,
‘westvirginia’: 7905,
‘whatimcrav’: 7917,
‘peanut’: 5275,
‘butter’: 1036,
‘worldseriesgam’: 8049,
‘penc’: 5292,
‘annoy’: 255,
‘player’: 5427,
‘shave’: 6369,
‘fool’: 2665,
‘vyvans’: 7767,
‘arod’: 331,
‘jeter’: 3757,
‘inning’: 3591,
‘sec’: 6270,
‘robertson’: 6043,
‘pitcher’: 5406,
‘astro’: 382,
‘mccarthi’: 4433,
‘outduel’: 5130,
‘keuchel’: 3874,
‘seri’: 6314,
‘final’: 2560,
‘nine’: 4915,
‘price’: 5571,
‘damn’: 1786,
‘directtv’: 2003,
‘blackout’: 744,
‘freak’: 2724,
‘satellit’: 6185,
‘douch’: 2096,
‘joe’: 3781,
‘bullpen’: 994,
‘flex’: 2619,
‘earn’: 2201,
‘went’: 7898,
‘gold’: 2966,
‘cruz’: 1721,
‘upper’: 7615,
‘lower’: 4252,
‘bodi’: 805,
‘strength’: 6905,
‘hmm’: 3321,
‘hitter’: 3319,
‘sale’: 6150,
‘whole’: 7951,
‘leav’: 4052,
‘gardner’: 2841,
‘tanaka’: 7072,
‘rest’: 5960,
‘plea’: 5430,
‘cash’: 1154,
‘bounc’: 855,
‘kay’: 3850,
‘keep’: 3856,
‘pineda’: 5392,
‘primo’: 5580,
‘tank’: 7076,
‘mlb’: 4609,
‘better’: 670,
‘seriou’: 6316,
‘sept’: 6309,
‘beaten’: 596,
‘three’: 7227,
‘motown’: 4695,
‘wast’: 7824,
‘ray’: 5803,
‘draught’: 2116,
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‘mere’: 4499,
‘scientif’: 6232,
‘indic’: 3549,
‘wrong’: 8076,
‘celebr’: 1183,
‘uniqu’: 7582,
‘indiviu’: 3552,
‘cancer’: 1098,
‘firefight’: 2576,
‘foxnew’: 2708,
‘cut’: 1759,
‘democrat’: 1894,
‘talk’: 7061,
‘fair’: 2442,
‘balanc’: 492,
‘network’: 4858,
‘liar’: 4112,
‘gouach’: 3006,
‘inktob’: 3585,
‘oldpaint’: 5053,
‘tiki’: 7262,
‘wahin’: 7783,
‘surf’: 6987,
‘bright’: 920,
‘happysunday’: 3179,
‘peac’: 5272,
‘fan’: 2462,
‘codi’: 1441,
‘zeller’: 8182,
‘nomin’: 4939,
‘inner’: 3589,
‘hang’: 3168,
‘sunday’: 6970,
‘suit’: 6965,
‘ncaa’: 4816,
‘hoosierdaddi’: 3387,
‘metal’: 4511,
‘honki’: 3374,
…}
In [7]:
text_counts.toarray()
Out[7]:
array([[0, 0, 0, …, 0, 0, 0],
[0, 0, 0, …, 0, 0, 0],
[0, 0, 0, …, 0, 0, 0],
…,
[0, 0, 0, …, 0, 0, 0],
[0, 0, 0, …, 0, 0, 0],
[0, 0, 0, …, 0, 0, 0]])
In [8]:
text_counts.shape #shape of count vector: 5593 docs (tweet) and 8212 unique words
Out[8]:
(5593, 8212)
In [9]:
# Array mapping from feature integer indices to feature name.
print(cv.get_feature_names())
[‘aaaaaaaaand’, ‘aan’, ‘aap’, ‘aaron’, ‘aaronmacgrud’, ‘abandonado’, ‘abbey’, ‘abbi’, ‘abc’, ‘abdelka’, ‘abduct’, ‘abdurahman’, ‘abel’, ‘aberdeen’, ‘abil’, ‘abl’, ‘abo’, ‘abort’, ‘abound’, ‘abouta’, ‘abraham’, ‘absent’, ‘absolut’, ‘absolv’, ‘absurd’, ‘abu’, ‘abund’, ‘abus’, ‘aca’, ‘acab’, ‘academ’, ‘accent’, ‘accept’, ‘access’, ‘accid’, ‘accident’, ‘accipit’, ‘accipitrida’, ‘accnt’, ‘accompani’, ‘accord’, ‘accordingli’, ‘account’, ‘acct’, ‘accur’, ‘accuraci’, ‘accus’, ‘acdc’, ‘ace’, ‘aceptar’, ‘acev’, ‘achil’, ‘acid’, ‘acknowledg’, ‘acl’, ‘acoust’, ‘acquir’, ‘across’, ‘act’, ‘actin’, ‘action’, ‘activ’, ‘activist’, ‘actor’, ‘actress’, ‘actual’, ‘adam’, ‘add’, ‘adderal’, ‘addict’, ‘address’, ‘admin’, ‘administr’, ‘admit’, ‘adonni’, ‘adopt’, ‘ador’, ‘adtr’, ‘adult’, ‘advanc’, ‘adventur’, ‘advertis’, ‘advic’, ‘advil’, ‘advis’, ‘aero’, ‘aerosmith’, ‘aerosol’, ‘afc’, ‘affect’, ‘affleck’, ‘afford’, ‘afghanistan’, ‘afi’, ‘afl’, ‘aflcio’, ‘afraid’, ‘africa’, ‘african’, ‘afternoon’, ‘afterward’, ‘age’, ‘agen’, ‘agenda’, ‘agent’, ‘agg’, ‘agholor’, ‘agn’, ‘ago’, ‘agoni’, ‘agre’, ‘ahah’, ‘ahahahh’, ‘ahead’, ‘ahh’, ‘ahha’, ‘ahhhahahaha’, ‘ahl’, ‘ahm’, ‘ahora’, ‘aid’, ‘aight’, ‘aim’, ‘aint’, ‘air’, ‘airbag’, ‘airlin’, ‘airport’, ‘ajmi’, ‘ajumma’, ‘aka’, ‘aklv’, ‘ala’, ‘alabama’, ‘alabamian’, ‘alarm’, ‘alaska’, ‘albino’, ‘album’, ‘alc’, ‘alco’, ‘alcohol’, ‘alec’, ‘alert’, ‘alex’, ‘alexa’, ‘alexand’, ‘alexandra’, ‘alfi’, ‘alfredo’, ‘algeria’, ‘alien’, ‘alik’, ‘aliv’, ‘all’, ‘alla’, ‘allah’, ‘alledg’, ‘allegi’, ‘allen’, ‘allergi’, ‘allezwiggo’, ‘alli’, ‘allllll’, ‘allow’, ‘allowin’, ‘allstar’, ‘allur’, ‘almeria’, ‘almond’, ‘almost’, ‘aloha’, ‘alon’, ‘along’, ‘alongsid’, ‘alot’, ‘aloud’, ‘alpha’, ‘alreadi’, ‘alright’, ‘alsarabsss’, ‘also’, ‘alsonobeermoney’, ‘alt’, ‘altar’, ‘although’, ‘altyd’, ‘alu’, ‘aluminum’, ‘alv’, ‘alway’, ‘alweer’, ‘alzheim’, ‘ama’, ‘amanda’, ‘amar’, ‘amaz’, ‘amazi’, ‘amazin’, ‘amazingtim’, ‘amber’, ‘amedit’, ‘amen’, ‘amend’, ‘america’, ‘american’, 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‘ballsack’, ‘balmi’, ‘balochistan’, ‘balt’, ‘baltimor’, ‘bam’, ‘bama’, ‘bambino’, ‘ban’, ‘banana’, ‘band’, ‘bandana’, ‘bandanna’, ‘bandwagon’, ‘bang’, ‘banger’, ‘bangladesh’, ‘banjo’, ‘bank’, ‘bankrupt’, ‘banner’, ‘bansal’, ‘banwagon’, ‘bape’, ‘bar’, ‘barack’, ‘barackobama’, ‘barbecu’, ‘barber’, ‘barbershop’, ‘barca’, ‘bare’, ‘barefoot’, ‘barg’, ‘bargain’, ‘barkley’, ‘barney’, ‘barnyard’, ‘barrel’, ‘barri’, ‘barrybond’, ‘bart’, ‘base’, ‘baseba’, ‘basebal’, ‘basement’, ‘bash’, ‘basi’, ‘basic’, ‘basketbal’, ‘bastard’, ‘bat’, ‘batch’, ‘bateman’, ‘bath’, ‘bathrob’, ‘bathroom’, ‘bathsalt’, ‘bathtim’, ‘batman’, ‘baton’, ‘batshit’, ‘batter’, ‘battl’, ‘bay’, ‘bayg’, ‘bayle’, ‘bayless’, ‘baylor’, ‘bazinga’, ‘bball’, ‘bbc’, ‘bbq’, ‘bbqd’, ‘bcu’, ‘bcuz’, ‘bday’, ‘bdub’, ‘beach’, ‘beak’, ‘beamthat’, ‘bean’, ‘beaner’, ‘beani’, ‘bear’, ‘bearbroth’, ‘beard’, ‘bearfac’, ‘beast’, ‘beat’, ‘beatbox’, ‘beaten’, ‘beauti’, ‘beavi’, ‘bec’, ‘becam’, ‘becca’, ‘becom’, ‘becuz’, ‘bed’, ‘bedroom’, ‘bedtim’, 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In [10]:
# neutral corpus
neucorpus=df[df[“Sentiment”]==0][“tokens”]
#print(neucorpus)
neutral_co=cv.get_feature_names()
print(neutral_co)
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In [11]:
# negative corpus
negcorpus=df[df[“Sentiment”]==1][“tokens”]
#print(negcorpus)
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In [12]:
# updata negative corpus and neutral corpus
negative_corpus= negative_co
neutral_corpus=[neutral_co]
for word in negative_co:
if word in neutral_co:
neutral_co.remove(word)
print(negative_corpus)
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‘tyron’, ‘tyson’, ‘ubb’, ‘uber’, ‘ucf’, ‘ucfpinkparti’, ‘ucla’, ‘udigg’, ‘udoni’, ‘ufc’, ‘uganda’, ‘ugg’, ‘ugh’, ‘ughh’, ‘ugli’, ‘ugliest’, ‘uhh’, ‘uhhh’, ‘uighur’, ‘uit’, ‘uitleggen’, ‘uknow’, ‘ulcer’, ‘ultim’, ‘umm’, ‘ump’, ‘umpir’, ‘una’, ‘unaffili’, ‘unanim’, ‘unarm’, ‘unattract’, ‘unbecom’, ‘unbeliev’, ‘unc’, ‘uncal’, ‘uncivil’, ‘uncivilis’, ‘uncl’, ‘uncletweet’, ‘uncomfort’, ‘unconstitut’, ‘uncrust’, ‘underachiev’, ‘undercov’, ‘underground’, ‘undergroundcultur’, ‘undermin’, ‘underperform’, ‘underr’, ‘understand’, ‘understood’, ‘underwood’, ‘undignifi’, ‘uneaten’, ‘uneduc’, ‘unemploy’, ‘unexcept’, ‘unexplain’, ‘unfathom’, ‘unfil’, ‘unfollow’, ‘unfortun’, ‘unfunni’, ‘unheal’, ‘unhelp’, ‘uni’, ‘unicorn’, ‘unifi’, ‘uniform’, ‘uninform’, ‘unintend’, ‘union’, ‘unionmemb’, ‘uniqu’, ‘unit’, ‘uniteblu’, ‘univers’, ‘uniy’, ‘unleash’, ‘unless’, ‘unlik’, ‘unn’, ‘unnatur’, ‘unpleas’, ‘unpredict’, ‘unpunish’, ‘unreal’, ‘unrecogniz’, ‘unreport’, ‘unsavori’, ‘unselfish’, ‘unsold’, ‘unsolicit’, ‘unthaw’, ‘unto’, ‘untru’, ‘unveil’, ‘unwash’, ‘uou’, ‘updat’, ‘upenn’, ‘upgrad’, ‘uphold’, ‘upload’, ‘upon’, ‘uppa’, ‘upper’, ‘uppiti’, ‘upport’, ‘upset’, ‘upsher’, ‘upton’, ‘upward’, ‘urban’, ‘urg’, ‘uri’, ‘url’, ‘urself’, ‘usa’, ‘usag’, ‘usc’, ‘use’, ‘useless’, ‘usen’, ‘usernam’, ‘usher’, ‘ushishi’, ‘usmnt’, ‘usual’, ‘utah’, ‘utampa’, ‘utcoach’, ‘utterli’, ‘uwi’, ‘uya’, ‘uza’, ‘vaak’, ‘vaca’, ‘vacat’, ‘vacay’, ‘vagina’, ‘valentin’, ‘valid’, ‘valu’, ‘valuabl’, ‘vampir’, ‘van’, ‘vandaan’, ‘vanessa’, ‘vanilla’, ‘vanish’, ‘vaniti’, ‘vapor’, ‘varieti’, ‘vccamelrac’, ‘vddie’, ‘ved’, ‘veer’, ‘vegan’, ‘vegasqueen’, ‘vegasshowgirl’, ‘veget’, ‘veil’, ‘vein’, ‘velvet’, ‘venn’, ‘verd’, ‘vermontgirlsbelik’, ‘vermontproblem’, ‘vers’, ‘versac’, ‘version’, ‘vertellen’, ‘vertic’, ‘vertonen’, ‘veryon’, ‘vestal’, ‘vet’, ‘veteran’, ‘vey’, ‘vez’, ‘vhia’, ‘via’, ‘viacom’, ‘vibe’, ‘vice’, ‘vick’, ‘victim’, ‘victor’, ‘victori’, ‘victoria’, ‘victoriasecret’, ‘vid’, ‘video’, ‘vietnames’, ‘view’, ‘vii’, ‘vike’, ‘vile’, ‘vilifi’, ‘villanueva’, ‘vin’, ‘vincent’, ‘vine’, ‘vinger’, ‘vinitahegwood’, ‘vinni’, ‘vinnysnotmadon’, ‘vintag’, ‘vintagebaublesnbit’, ‘vinyl’, ‘vinylgasm’, ‘viola’, ‘violat’, ‘violenc’, ‘vip’, ‘viral’, ‘virginia’, ‘virgo’, ‘virtu’, ‘virtual’, ‘viru’, ‘visibl’, ‘vision’, ‘visionari’, ‘visit’, ‘visual’, ‘viswa’, ‘vital’, ‘vitaligro’, ‘vitamin’, ‘vite’, ‘vivek’, ‘vivir’, ‘vlee’, ‘vma’, ‘vnd’, ‘vocabulari’, ‘vocal’, ‘vodka’, ‘voeckler’, ‘voem’, ‘voguet’, ‘voic’, ‘volg’, ‘volunt’, ‘vomit’, ‘voor’, ‘voorstellen’, ‘vote’, ‘voter’, ‘votewendydavi’, ‘votingmatt’, ‘vraag’, ‘vragen’, ‘vroomfroom’, ‘vrouwen’, ‘vuelta’, ‘vyvans’, ‘waaay’, ‘waahh’, ‘waar’, ‘wack’, ‘wacko’, ‘waco’, ‘wacthh’, ‘wad’, ‘wadi’, ‘wae’, ‘waffl’, ‘wag’, ‘wage’, ‘wagon’, ‘wah’, ‘wahin’, ‘wais’, ‘waist’, ‘wait’, ‘waitress’, ‘waiv’, ‘wake’, ‘wale’, ‘walk’, ‘walker’, ‘walkin’, ‘walkoff’, ‘walkout’, ‘wall’, ‘wallac’, ‘wallet’, ‘walmart’, ‘wan’, ‘wankin’, ‘wannab’, ‘want’, ‘wantin’, ‘wapo’, ‘war’, ‘warehous’, ‘warfar’, ‘warm’, ‘warn’, ‘warner’, ‘warren’, ‘warrior’, ‘warsaw’, ‘wash’, ‘washer’, ‘washington’, ‘washingtondc’, ‘washpost’, ‘wasnt’, ‘wasp’, ‘waspfact’, ‘wassup’, ‘wast’, ‘wat’, ‘watch’, ‘watchin’, ‘watchlikeak’, ‘water’, ‘watermelon’, ‘waterwalk’, ‘watic’, ‘watson’, ‘wave’, ‘waverli’, ‘waveslid’, ‘wax’, ‘waxi’, ‘waxman’, ‘waxx’, ‘way’, ‘wayan’, ‘waylon’, ‘wayn’, ‘wayofwad’, ‘wcar’, ‘wcvb’, ‘wcvbpoli’, ‘wcw’, ‘weak’, ‘weaken’, ‘wealth’, ‘wealthiest’, ‘weapon’, ‘wear’, ‘wearabl’, ‘wearerepublicnotregim’, ‘weari’, ‘weather’, ‘weav’, ‘web’, ‘webb’, ‘webber’, ‘websit’, ‘wec’, ‘wed’, ‘wedg’, ‘wedlock’, ‘wednesday’, ‘wee’, ‘weed’, ‘week’, ‘weekend’, ‘weeni’, ‘weep’, ‘weepi’, ‘weer’, ‘weet’, ‘weezi’, ‘wefo’, ‘wei’, ‘weibo’, ‘weight’, ‘weiner’, ‘weird’, ‘weirdo’, ‘weirdtweet’, ‘wel’, ‘welcom’, ‘welfar’, ‘well’, ‘welp’, ‘wembley’, ‘wen’, ‘wench’, ‘wendi’, ‘wenger’, ‘went’, ‘wept’, ‘wer’, ‘werd’, ‘west’, ‘westbrook’, ‘western’, ‘westvirginia’, ‘wet’, ‘wetback’, ‘weve’, ‘wexican’, ‘wha’, ‘whack’, ‘whale’, ‘what’, ‘whataburg’, ‘whatchu’, ‘whatev’, ‘whatimcrav’, ‘whatnot’, ‘whatsoev’, ‘whatta’, ‘whatwashedo’, ‘wheel’, ‘whenev’, ‘wher’, ‘wherescolman’, ‘whether’, ‘whiffi’, ‘whigger’, ‘whilst’, ‘whine’, ‘whineytweet’, ‘whip’, ‘whiskey’, ‘whisper’, ‘whistl’, ‘white’, ‘whitecultur’, ‘whitehous’, ‘whiteknight’, ‘whitepow’, ‘whitesnak’, ‘whitest’, ‘whitetear’, ‘whitetrash’, ‘whitey’, ‘whiti’, ‘whitman’, ‘whizz’, ‘whoa’, ‘whoever’, ‘whole’, ‘wholesal’, ‘whomp’, ‘whoo’, ‘whooooo’, ‘whoooooaaa’, ‘whoop’, ‘whore’, ‘whose’, ‘whse’, ‘whydidigoogl’, ‘whynot’, ‘wib’, ‘wibbler’, ‘wick’, ‘wide’, ‘wif’, ‘wife’, ‘wifebeat’, ‘wifey’, ‘wifi’, ‘wig’, ‘wigga’, ‘wigger’, ‘wiki’, ‘wild’, ‘wildlif’, ‘will’, ‘willen’, ‘william’, ‘williamsc’, ‘wilma’, ‘wilson’, ‘wim’, ‘win’, ‘wind’, ‘windoowww’, ‘window’, ‘windshield’, ‘windturbin’, ‘wine’, ‘wing’, ‘wink’, ‘winkler’, ‘winner’, ‘winnnn’, ‘winston’, ‘winter’, ‘wipe’, ‘wire’, ‘wisconsin’, ‘wise’, ‘wish’, ‘wishin’, ‘wishywashi’, ‘wit’, ‘witch’, ‘witcho’, ‘without’, ‘wiunion’, ‘wiz’, ‘wizkid’, ‘wld’, ‘wmn’, ‘wnba’, ‘woah’, ‘woahh’, ‘wobbl’, ‘wobbuffet’, ‘wobn’, ‘wog’, ‘woke’, ‘woman’, ‘wonder’, ‘wonderbaarlijk’, ‘wondertrad’, ‘wong’, ‘wont’, ‘woo’, ‘wood’, ‘wooden’, ‘woodi’, ‘woof’, ‘woohoo’, ‘wooooow’, ‘woot’, ‘wop’, ‘wor’, ‘word’, ‘worden’, ‘wore’, ‘work’, ‘worker’, ‘workforc’, ‘workout’, ‘world’, ‘worldcup’, ‘worldseri’, ‘worldseriesgam’, ‘worm’, ‘worri’, ‘wors’, ‘worship’, ‘worst’, ‘worstpeanutsspecialev’, ‘worth’, ‘worthless’, ‘would’, ‘woulda’, ‘wouldmakealottasens’, ‘wouldnt’, ‘wound’, ‘wow’, ‘woyk’, ‘wpxi’, ‘wrap’, ‘wrath’, ‘wreck’, ‘wrench’, ‘wrestler’, ‘wrist’, ‘write’, ‘writer’, ‘writetip’, ‘written’, ‘wrong’, ‘wrote’, ‘wtfiswrongwithnebraska’, ‘wtl’, ‘wtmf’, ‘wut’, ‘wuz’, ‘wuzzi’, ‘wvnextin’, ‘wvu’, ‘wwc’, ‘wyatt’, ‘wyom’, ‘xbla’, ‘xbox’, ‘xcx’, ‘xfactor’, ‘xxx’, ‘yacht’, ‘yah’, ‘yahshua’, ‘yahweh’, ‘yal’, ‘yall’, ‘yam’, ‘yangervi’, ‘yank’, ‘yanke’, ‘yankeeographi’, ‘yankeestadium’, ‘yaoooooooo’, ‘yap’, ‘yard’, ‘yardi’, ‘yaselv’, ‘yasser’, ‘yasss’, ‘yay’, ‘yaya’, ‘yea’, ‘yeaaaaahhhh’, ‘yeah’, ‘year’, ‘yee’, ‘yeeye’, ‘yeezu’, ‘yell’, ‘yellow’, ‘yelp’, ‘yep’, ‘yer’, ‘yes’, ‘yesterday’, ‘yeswedid’, ‘yet’, ‘yezidi’, ‘yid’, ‘yma’, ‘yob’, ‘yoga’, ‘yok’, ‘yokel’, ‘yokocho’, ‘yolk’, ‘yolo’, ‘york’, ‘yoself’, ‘yost’, ‘yostknowsmost’, ‘you’, ‘youcansitwithu’, ‘youll’, ‘youn’, ‘young’, ‘younger’, ‘younnnnng’, ‘youpeopl’, ‘your’, ‘youth’, ‘youtub’, ‘youuuuu’, ‘youz’, ‘youza’, ‘yowza’, ‘yrd’, ‘ysrah’, ‘yuck’, ‘yuh’, ‘yuhhh’, ‘yummi’, ‘yun’, ‘yung’, ‘yup’, ‘yusa’, ‘yute’, ‘yyyyyeelllloooowwwwww’, ‘zack’, ‘zak’, ‘zal’, ‘zappa’, ‘zebra’, ‘zegj’, ‘zegt’, ‘zei’, ‘zeitgeist’, ‘zelfd’, ‘zeller’, ‘zerah’, ‘zero’, ‘zertec’, ‘zich’, ‘zigeun’, ‘zihni’, ‘zij’, ‘zijn’, ‘zim’, ‘zima’, ‘zimmerman’, ‘zion’, ‘zionist’, ‘zip’, ‘zipperhead’, ‘zlatan’, ‘zoal’, ‘zoe’, ‘zog’, ‘zolang’, ‘zombi’, ‘zone’, ‘zoo’, ‘zookeep’, ‘zoom’, ‘zorro’, ‘zucchini’, ‘zzzquil’, ‘zzzzzz’]
In [13]:
print(neutral_corpus)
[[]]
In [14]:
from sklearn.model_selection import train_test_split
# split the the dataset into training set and test set
X_train, X_test, y_train, y_test = train_test_split(
text_counts, df[‘Sentiment’], test_size=0.2, random_state=1)
In [15]:
# word embeeding method 1:Bag of Words
# algorithm 1: Naive Bayes algorithm
from sklearn.naive_bayes import MultinomialNB
from sklearn import metrics
from sklearn.metrics import accuracy_score
# generate the model
clf = MultinomialNB().fit(X_train, y_train)
predicted= clf.predict(X_test)
# create a variable to store the result of accuracy
MNB_bow_accuracy = accuracy_score(y_test, predicted)
# check the accuracy rate, keep two decimal places
print(“NB_bow Accuracy by using Bag of Words: “, round(MNB_bow_accuracy,2))
NB_bow Accuracy by using Bag of Words: 0.92
In [16]:
# model evaluation: classification report
from sklearn.metrics import classification_report
print(“NB_bow Classification Report:”)
print(classification_report(y_test,predicted))
NB_bow Classification Report:
precision recall f1-score support
0 0.96 0.93 0.95 846
1 0.80 0.89 0.84 273
accuracy 0.92 1119
macro avg 0.88 0.91 0.89 1119
weighted avg 0.92 0.92 0.92 1119
In [17]:
# model evaluation: confusion matrix
from sklearn.metrics import confusion_matrix
import pandas as pd
# generate the table
cm_table1 = pd.DataFrame(confusion_matrix(y_test,predicted), columns=[“Negative”, “Neutral”],index=[“Negative”,”Neutral”])
print(“Confusion Matrix:”)
cm_table1
Confusion Matrix:
Out[17]:
Negative
Neutral
Negative
786
60
Neutral
31
242
In [18]:
# model evaluation: confusion matrix
import matplotlib.pyplot as plt
from sklearn.metrics import plot_confusion_matrix
# generate the plot
plot_confusion_matrix(clf, X_test, y_test)
plt.title(“NB_bow_Confusion Matrix”)
plt.show()

In [19]:
# use cross-validation.
from sklearn.model_selection import cross_val_score
# generate the cross validation of the model
MNB_scores = cross_val_score(clf,X_train, y_train)
# create a variable to store the mean of cross validation scores
MNB_mean = MNB_scores.mean()
print(“NB_bow cross validation mean score:”, round(MNB_mean,3))
NB_bow cross validation mean score: 0.893
In [20]:
# word embeeding method 1:Bag of Words
# algorithm 2: Random Forest algorithm
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
# generate the RF model
text_classifier = RandomForestClassifier(n_estimators=200, random_state=0)
clf3 = text_classifier.fit(X_train, y_train)
predictions_rf = clf3.predict(X_test)
# create a variable to store the result of accuracy
RF_bow_accuracy = accuracy_score(y_test, predictions_rf)
# check the accuracy rate, keep two decimal places
print(“RF_bow Accuracy by using tfidf: “, round(RF_bow_accuracy,2))
RF_bow Accuracy by using tfidf: 0.93
In [21]:
# model evaluation: classification report
from sklearn.metrics import classification_report
print(“RF_bow Classification Report:”)
print(classification_report(y_test, predictions_rf))
RF_bow Classification Report:
precision recall f1-score support
0 0.97 0.94 0.96 846
1 0.84 0.90 0.87 273
accuracy 0.93 1119
macro avg 0.90 0.92 0.91 1119
weighted avg 0.94 0.93 0.93 1119
In [22]:
# model evaluation: confusion matrix
from sklearn.metrics import confusion_matrix
import pandas as pd
# generate the table
cm_table2 = pd.DataFrame(confusion_matrix(y_test,predictions_rf), columns=[“Negative”, “Neutral”],index=[“Negative”,”Neutral”])
print(“Confusion Matrix:”)
cm_table2
Confusion Matrix:
Out[22]:
Negative
Neutral
Negative
798
48
Neutral
26
247
In [23]:
# model evaluation: confusion matrix
import matplotlib.pyplot as plt
from sklearn.metrics import plot_confusion_matrix
# generate the plot
plot_confusion_matrix(clf3, X_test, y_test)
plt.title(“RF_bow_Confusion Matrix”)
plt.show()

In [24]:
# use Cross-validation
from sklearn.model_selection import cross_val_score
# generate the cross validation of the model
RF_bow_scores = cross_val_score(clf3,X_train, y_train)
# create a variable to store the mean of cross validation scores
RF_bow_mean = RF_bow_scores.mean()
print(“RF cross validation mean score:”, round(RF_bow_mean,2))
RF cross validation mean score: 0.94
In [25]:
# feature generation
# word embeeding method 2: TF-IDF
from sklearn.feature_extraction.text import TfidfVectorizer
tf = TfidfVectorizer()
m = cv.fit_transform(df[‘tokens’])
text_tf = m
In [26]:
from sklearn.model_selection import train_test_split
# split the the dataset into training set and test set
X_train, X_test, y_train, y_test = train_test_split(
text_tf, df[‘Sentiment’], test_size=0.2, random_state=123)
In [27]:
# word embeeding method 2: TF-IDF
# algorithm 1: Naive Bayes algorithm
from sklearn.naive_bayes import MultinomialNB
from sklearn import metrics
from sklearn.metrics import accuracy_score
# generate model
clf1 = MultinomialNB().fit(X_train, y_train)
predicted1 = clf1.predict(X_test)
MNB_tf_accuracy = accuracy_score(y_test, predicted1)
print(“NB Accuracy:”, round(MNB_tf_accuracy,2))
NB Accuracy: 0.91
In [28]:
# model evaluation: classification report
from sklearn.metrics import classification_report
print(“NB_tfidf Classification Report:”)
print(classification_report(y_test,predicted1))
NB_tfidf Classification Report:
precision recall f1-score support
0 0.96 0.92 0.94 842
1 0.79 0.88 0.83 277
accuracy 0.91 1119
macro avg 0.87 0.90 0.89 1119
weighted avg 0.92 0.91 0.91 1119
In [29]:
# model evaluation: confusion matrix
from sklearn.metrics import confusion_matrix
import pandas as pd
# generate the table
cm_table3 = pd.DataFrame(confusion_matrix(y_test,predicted1), columns=[“Negative”, “Neutral”],index=[“Negative”,”Neutral”])
print(“Confusion Matrix:”)
cm_table3
Confusion Matrix:
Out[29]:
Negative
Neutral
Negative
777
65
Neutral
34
243
In [30]:
# model evaluation: confusion matrix
import matplotlib.pyplot as plt
from sklearn.metrics import plot_confusion_matrix
# generate the plot
plot_confusion_matrix(clf1, X_test, y_test)
plt.title(“NB_tfidf_Confusion Matrix”)
plt.show()

In [31]:
# use cross-validation.
from sklearn.model_selection import cross_val_score
# generate the cross validation of the model
MNB_tf_scores = cross_val_score(clf1,X_train, y_train)
# create a variable to store the mean of cross validation scores
MNB_tf_mean = MNB_tf_scores.mean()
print(“NB_tfidf cross validation mean score:”, round(MNB_tf_mean,2))
NB_tfidf cross validation mean score: 0.9
In [32]:
# word embeeding method 2: TF-IDF
# algorithm 2: use Random Forest algorithm
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
# generate RF model
text_classifier = RandomForestClassifier(n_estimators=200, random_state=0)
clf2 = text_classifier.fit(X_train, y_train)
predictions = clf2.predict(X_test)
# create a variable to store the model accuracy
RF_accuracy = accuracy_score(y_test, predictions)
print(“Random Forest Accuracy by using tfidf: “, round(RF_accuracy,2))
Random Forest Accuracy by using tfidf: 0.94
In [33]:
# model evaluation: classification report
from sklearn.metrics import classification_report
print(“RF_tfidf Classification Report:”)
print(classification_report(y_test,predictions))
RF_tfidf Classification Report:
precision recall f1-score support
0 0.97 0.95 0.96 842
1 0.87 0.90 0.88 277
accuracy 0.94 1119
macro avg 0.92 0.93 0.92 1119
weighted avg 0.94 0.94 0.94 1119
In [34]:
# model evaluation: confusion matrix
from sklearn.metrics import confusion_matrix
import pandas as pd
# generate the table
cm_table4 = pd.DataFrame(confusion_matrix(y_test,predictions), columns=[“Negative”, “Neutral”],index=[“Negative”,”Neutral”])
print(“Confusion Matrix:”)
cm_table4
Confusion Matrix:
Out[34]:
Negative
Neutral
Negative
803
39
Neutral
27
250
In [35]:
# model evaluation: confusion matrix
import matplotlib.pyplot as plt
from sklearn.metrics import plot_confusion_matrix
# generate the plot
plot_confusion_matrix(clf2, X_test, y_test)
plt.title(“NB_tfidf_Confusion Matrix”)
plt.show()

In [36]:
# use cross-validation.
from sklearn.model_selection import cross_val_score
# generate the cross validation of the model
RF_tf_scores = cross_val_score(clf2,X_train, y_train)
# create a variable to store the mean of cross validation scores
RF_tf_mean = RF_tf_scores.mean()
print(“RF_tfidf cross validation mean score:”, round(RF_tf_mean,2))
RF_tfidf cross validation mean score: 0.94
In [37]:
# conclusion part
# create a dictionary to store the results
k = {‘Classifier’: [‘Naive Bayes_bow’, ‘Naive Bayes_tfidf’, ‘Random Forest_bow’, ‘Random Forest_tfidf’],
‘Accuracy’: [MNB_bow_accuracy, MNB_tf_accuracy, RF_bow_accuracy, RF_tf_mean],
‘Cross-value’: [MNB_mean, MNB_tf_mean, RF_bow_mean, RF_accuracy]}
# create a variable to store the dataframe of the results
result_df = pd.DataFrame(data=k)
# check the table
result_df
Out[37]:
Classifier
Accuracy
Cross-value
0
Naive Bayes_bow
0.918677
0.893159
1
Naive Bayes_tfidf
0.911528
0.896741
2
Random Forest_bow
0.933870
0.935625
3
Random Forest_tfidf
0.935407
0.941019
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ref: https://www.datacamp.com/community/tutorials/text-analytics-beginners-nltkref: https://github.com/vedant-95/Twitter-Hate-Speech-Detection/blob/master/Final%20working%20code.ipynbCross validation ref: https://towardsdatascience.com/cross-validation-explained-evaluating-estimator-performance-e51e5430ff85模型预测 ref: https://www.cnblogs.com/jclian91/p/10886031.html
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