In [1]:
import pandas as pd;
import numpy as np;
In [2]:
regular_tweets = pd.read_csv('processed_tweets/regular_df_eng.csv')
sarcastic_tweets = pd.read_csv('processed_tweets/sarcastic_df_eng.csv')
In [3]:
regular_tweets = regular_tweets.drop( ['Unnamed: 0'], axis=1)
sarcastic_tweets = sarcastic_tweets.drop( ['Unnamed: 0'], axis=1)
In [4]:
# ALL CAPS COUNT
sarcastic_tweets['AllCapsCount'] = [sum([k.isupper() for k in n.split(" ")]) for n in sarcastic_tweets['0']]
regular_tweets['AllCapsCount'] = [sum([k.isupper() for k in n.split(" ")]) for n in regular_tweets['0']]
In [5]:
sarcastic_tweets
Out[5]:
0
type
English
ToUser
Hashtags
AllCapsCount
0
Thanks
sarcastic
1
0
0
0
1
Top tip. To illicit a \"thank you\" from some...
sarcastic
1
0
1
0
2
Thanks to whoever just threw the bag of waterm...
sarcastic
1
0
1
0
3
yes let's #EndFathersDay because the mother i...
sarcastic
1
0
1
0
4
Well it's just gonna turn into a lovely day
sarcastic
1
0
0
0
5
Nothing to see here, move along Lerner's L...
sarcastic
1
0
0
1
6
So who does Campbell play for?
sarcastic
1
0
0
0
7
@JamesBraginton @STEM08 @thinkprogress \nJames...
sarcastic
1
1
0
1
8
Does this make me fancy? #imsofancyyoualreadyk...
sarcastic
1
0
1
0
9
I love that Arequipa just shuts the water off ...
sarcastic
1
0
0
1
10
Everyone's at Notre Dame and I'm just sitting ...
sarcastic
1
0
1
0
11
Tweet of the day!!!! \ud83d\ude1c Holy shit...
sarcastic
1
0
0
0
12
@LUTZLOVER43
sarcastic
1
1
0
1
13
#orgasm
sarcastic
1
0
1
0
14
I hate to see Luis Suarez get injured, returni...
sarcastic
1
0
0
1
15
@jaycutlersux right bc Bush\/Cheney were total...
sarcastic
1
1
0
0
16
@TPoloking don't wrry
sarcastic
1
1
0
0
17
wow today is going just absolutely SPECTACULAR
sarcastic
1
0
0
1
18
Looking forward to playing Costa Rica what wit...
sarcastic
1
0
0
0
19
After the last friendly, I can only be happy a...
sarcastic
1
0
0
1
20
Tithes paid. Bills paid. Now to go clock anoth...
sarcastic
1
0
0
1
21
Clive thought that was in, great commentary ...
sarcastic
1
0
1
0
22
@New0rleans_Lady @Aaron_RS Lol Thats they fair...
sarcastic
1
1
0
0
23
\"Nothing says 'come to me baby' like a sexy p...
sarcastic
1
0
0
0
24
@ScottCubs36 why is white so positive, you rac...
sarcastic
1
1
1
0
25
I just love that when a celebrity or a youtube...
sarcastic
1
0
1
1
26
Loving the football tonight \ud83d\udc9c\ud83d...
sarcastic
1
0
1
0
27
I just pulled off 3 ticks from my hip yay! #...
sarcastic
1
0
1
1
28
@francescaacox apparently people have been men...
sarcastic
1
1
0
0
29
The only downside is that it wouldn't destroy ...
sarcastic
1
0
0
0
...
...
...
...
...
...
...
130021
Like seriously! :D
sarcastic
1
0
0
1
130022
works sometimes.
sarcastic
1
0
0
0
130023
In other news, all MQM bhayya's have decided t...
sarcastic
1
0
1
2
130024
@WesleyLowery Yeah, just a notch above Andrea ...
sarcastic
1
1
0
0
130025
In other news, all MQM bhayya's have decided t...
sarcastic
1
0
1
2
130026
REGAIN YOUR POISE HYSTERICAL WOMAN-Careful, bo...
sarcastic
1
0
1
4
130027
Ya that was me!
sarcastic
1
0
0
0
130028
She always cooks me the best gourmet meals...t...
sarcastic
1
0
1
1
130029
Did I miss the week long build up of @KingJame...
sarcastic
1
1
1
1
130030
Bite an opposing player in the World Cup, get ...
sarcastic
1
1
1
0
130031
Who is this Lebron James that everybody is spe...
sarcastic
1
0
0
1
130032
I love how certain circumstances can be both c...
sarcastic
1
0
0
1
130033
It's real mature to talk smack behind someone'...
sarcastic
1
0
1
1
130034
@Tolstoved @IDFSpokesperson I changed my mind,...
sarcastic
1
1
0
1
130035
@TU_Keem
sarcastic
1
1
0
0
130036
So wait. Is LeBron James going back to Clevela...
sarcastic
1
0
1
1
130037
$BBRY Better listen to TMF BlackBerry is dead ...
sarcastic
1
0
0
2
130038
@KindredShins REGAIN YOUR POISE HYSTERICAL WOM...
sarcastic
1
1
0
4
130039
I'm really stoked that @espn came back with th...
sarcastic
1
1
0
0
130040
In case you didn't know, LeBron is signing wit...
sarcastic
1
0
0
0
130041
Hey #Cleveland looks like you are going to get...
sarcastic
1
0
1
0
130042
@Wac4 GREAT! Jeremy Lin might becoming to the ...
sarcastic
1
1
0
1
130043
#bitchvice studios is moving! So. Much. Fun. ...
sarcastic
1
0
1
0
130044
Microsoft updates are so important that I don'...
sarcastic
1
0
0
1
130045
@erikld Let me try to reproduce
sarcastic
1
1
0
0
130046
And the pursuit of kevin love has picked up. G...
sarcastic
1
0
0
1
130047
Forgot how delightful an experience travelling...
sarcastic
1
0
1
0
130048
Not sure if everyone has heard, but Labron Jam...
sarcastic
1
0
1
0
130049
That heat fan wasn't mad at all
sarcastic
1
0
0
0
130050
I always roll my eyes when ppl say \"Your face...
sarcastic
1
0
0
2
130051 rows × 6 columns
Unigram counts (top 5000 unigrams)
In [7]:
from sklearn.feature_extraction.text import CountVectorizer
In [8]:
vector = CountVectorizer(max_features=5000).fit(open('processed_tweets/sarcastic_df_eng.csv'))
In [9]:
vector.get_feature_names()
Out[9]:
['00',
'000',
'10',
'100',
'1000',
'10k',
'10pm',
'11',
'11pm',
'12',
'120',
'13',
'13th',
'14',
'140',
'15',
'16',
'17',
'18',
'19',
'1am',
'1n1t',
'1st',
'20',
'200',
'2010',
'2013',
'2014',
'2014th',
'2015',
'21',
'21st',
'22',
'23',
'24',
'25',
'26',
'27',
'28',
'29',
'2am',
'2nd',
'30',
'300',
'30am',
'30pm',
'31',
'32',
'35',
'38',
'39',
'3am',
'3rd',
'40',
'400',
'44',
'45',
'48',
'4am',
'4th',
'4thofjuly',
'50',
'500',
'5am',
'5sos',
'5th',
'60',
'6th',
'70',
'75',
'76ers',
'7th',
'80',
'81',
'85',
'8am',
'90',
'911',
'95',
'99',
'9th',
'__',
'___',
'____',
'aa',
'aaron',
'ab',
'abandoned',
'abc',
'ability',
'able',
'abortion',
'about',
'above',
'absolute',
'absolutely',
'abt',
'abuse',
'ac',
'accent',
'accept',
'acceptable',
'access',
'accident',
'accidentally',
'accomplished',
'accomplishment',
'according',
'account',
'accounting',
'accounts',
'accurate',
'accused',
'ace',
'achievement',
'across',
'act',
'acting',
'action',
'actions',
'activity',
'actor',
'acts',
'actual',
'actually',
'ad',
'adam',
'adams',
'add',
'added',
'addicted',
'addiction',
'adding',
'addition',
'address',
'adds',
'adelaide',
'administration',
'admire',
'admit',
'adorable',
'adrian',
'ads',
'adult',
'advance',
'advanced',
'advantage',
'adventure',
'adventures',
'advice',
'afc',
'afford',
'afl',
'afraid',
'africa',
'african',
'after',
'afternoon',
'again',
'against',
'age',
'agency',
'agenda',
'agent',
'agents',
'ages',
'aggressive',
'ago',
'agree',
'agreed',
'ah',
'aha',
'ahead',
'ahh',
'ahhh',
'aid',
'ain',
'aint',
'air',
'aircanada',
'airlines',
'airport',
'aj',
'ajkreisberg',
'aka',
'al',
'alan',
'alanashby',
'alarm',
'album',
'alcohol',
'alert',
'alex',
'alexis',
'algeria',
'alive',
'all',
'allegations',
'allegiance',
'allergic',
'allergies',
'alliance',
'allow',
'allowed',
'allowing',
'almost',
'alone',
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'alot',
'already',
'alright',
'also',
'although',
'always',
'am',
'amaze',
'amazes',
'amazing',
'amazon',
'america',
'american',
'americanair',
'americans',
'amirite',
'among',
'amount',
'amp',
'amtrak',
'amusement',
'an',
'analysis',
'ananvii',
'and',
'andrew',
'android',
'andy',
'angel',
'angels',
'anger',
'angry',
'animal',
'animals',
'ankle',
'ann',
'anncoulter',
'anniversary',
'announced',
'announcement',
'announcer',
'announcers',
'annoy',
'annoyed',
'annoying',
'annual',
'another',
'answer',
'answered',
'answering',
'answers',
'ant',
'anthem',
'anthony',
'anthonycumia',
'anti',
'antonio',
'anxiety',
'any',
'anybody',
'anymore',
'anyone',
'anything',
'anytime',
'anyway',
'anyways',
'anywhere',
'ap',
'apart',
'apartment',
'apologies',
'apologise',
'apologize',
'apology',
'app',
'apparently',
'appear',
'appears',
'applause',
'apple',
'apply',
'appointment',
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'appreciate',
'appreciated',
'april',
'apt',
'arab',
'are',
'area',
'aren',
'arena',
'arent',
'arg',
'argentina',
'argue',
'arguing',
'argument',
'arguments',
'argvsbih',
'arizona',
'arm',
'armed',
'arms',
'army',
'around',
'arrested',
'arrival',
'arrive',
'arrived',
'arsenal',
'art',
'article',
'articles',
'artist',
'artistic',
'as',
'asap',
'asg',
'ashamed',
'aside',
'ask',
'asked',
'asking',
'asks',
'asleep',
'ass',
'assad',
'asshole',
'assholes',
'assume',
'astros',
'asylum',
'at',
'ate',
'atheist',
'athlete',
'athletes',
'atl',
'atlanta',
'atleast',
'atm',
'atmosphere',
'att',
'attack',
'attacks',
'attcustomercare',
'attempt',
'attend',
'attendance',
'attention',
'attitude',
'attractive',
'audience',
'august',
'aunt',
'auspol',
'aussie',
'austin',
'australia',
'authority',
'autistic',
'auto',
'autocorrect',
'automatically',
'available',
'average',
'avi',
'avoid',
'aw',
'awake',
'award',
'awards',
'aware',
'awareness',
'away',
'awe',
'awesome',
'awful',
'awh',
'awhile',
'awkward',
'aww',
'awww',
'aye',
'b4',
'babe',
'babies',
'baby',
'babysit',
'babysitting',
'back',
'backed',
'background',
'backs',
'backwards',
'bad',
'badass',
'badly',
'bae',
'bag',
'bags',
'baker',
'balanced',
'ball',
'balls',
'balotelli',
'baltimore',
'ban',
'banana',
'band',
'bands',
'bandwagon',
'bang',
'bank',
'banks',
'banned',
'banter',
'bar',
'barackobama',
'barca',
'barely',
'barrel',
'base',
'baseball',
'based',
'basement',
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'basic',
'basically',
'basis',
'basketball',
'bastard',
'bat',
'bathroom',
'battery',
'batting',
'battle',
'bay',
'bb15',
'bb16',
'bbad',
'bbc',
'bbcnews',
'bbcsport',
'bbcworldcup',
'bbq',
'bbuk',
'bc',
'bcuz',
'bday',
'be',
'beach',
'bear',
'beard',
'beast',
'beat',
'beating',
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'beautiful',
'beauty',
'became',
'because',
'become',
'becoming',
'bed',
'bedroom',
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'begin',
'beginning',
'behavior',
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'being',
'bel',
'belgian',
'belgium',
'beliefs',
'believe',
'believed',
'bell',
'belong',
'belongs',
'below',
'bench',
'benefit',
'benefits',
'benefitsbritain',
'benghazi',
'beroyalkc',
'besides',
'best',
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'betawards',
'better',
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'beyonce',
'beyond',
'bf',
'bff',
'bias',
'biased',
'bible',
'big',
'bigbrother',
'bigger',
'biggest',
'bike',
'bikes',
'bill',
'billion',
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'billy',
'bing',
'bio',
'bird',
'birds',
'birth',
'birthday',
'bit',
'bitch',
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'bj',
'bjp',
'black',
'blackhawks',
'blackrepublican',
'blacks',
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'blaming',
'blast',
'bleeding',
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'blessed',
'blew',
'blind',
'blinker',
'block',
'blocked',
'blog',
'blonde',
'blood',
'bloody',
'blow',
'blowing',
'blown',
'blue',
'bluejays',
'board',
'boat',
'bob',
'body',
'bold',
'bomb',
'bombs',
'bond',
'bonus',
'boo',
'boob',
'boobs',
'book',
'bookbuzzr',
'books',
'boom',
'boomboom',
'boost',
'boots',
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'border',
'bored',
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'boring',
'born',
'bosh',
'bosnia',
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'boston',
'both',
'bother',
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'bottle',
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'bottom',
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'bound',
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'bowl',
'box',
'boy',
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'bra',
'braces',
'brachi',
'bradley',
'brager',
'brain',
'brains',
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'brandon',
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'brave',
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'bravo',
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'bravscol',
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'brazil',
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'brazilvsgermany',
'bread',
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'bro',
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'brookeg105',
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'carlisle',
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'carmelo',
'carras16',
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'cavs',
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'cc',
'ccot',
'cd',
'cdnpoli',
'cease',
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'celebrity',
'cell',
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'central',
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'chaos',
'character',
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'charger',
'charges',
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'charm',
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'chat',
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'check',
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'checking',
'cheer',
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'cheney',
'chess',
'chest',
'chew',
'chi',
'chicago',
'chick',
'chicken',
'chiellini',
'child',
'children',
'chile',
'chiles',
'chill',
'chillin',
'chilling',
'china',
'chipotle',
'chips',
'chocolate',
'choice',
'choices',
'choose',
'chose',
'chosen',
'chris',
'chris_broussard',
'christ',
'christian',
'christians',
'christmas',
'chuck',
'church',
'cia',
'cigarette',
'cinema',
'circle',
'citizens',
'city',
'civil',
'claim',
'claiming',
'claims',
'clap',
'class',
'classes',
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'classy',
'cle',
'clean',
'cleaned',
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'clear',
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'click',
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'clinton',
'clock',
'close',
'closed',
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'closest',
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'clothes',
'cloud',
'clown',
'clowns',
'club',
'clubs',
'clue',
'clueless',
'clutch',
'cm',
'cmon',
'cn',
'cnblog',
'cnn',
'co',
'coach',
'coaches',
'coaching',
'coast',
'cocacola',
'cochran',
'cock',
'cod',
'code',
'cody',
'coffee',
'coincidence',
'coke',
'col',
'cold',
'collected',
'collection',
'college',
'colombia',
'color',
'combination',
'comcast',
'come',
'comeback',
'comedian',
'comedy',
'comes',
'comfortable',
'comforting',
'comfy',
'comic',
'comics',
'comin',
'coming',
'comment',
'commentary',
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'commercial',
'commercials',
'common',
'communication',
'community',
'commute',
'companies',
'company',
'compete',
'competition',
'competitive',
'complain',
'complaining',
'complete',
'completely',
'complicated',
'compliment',
'computer',
'computers',
'concept',
'concerned',
'concert',
'conclusion',
'concussions',
'condescending',
'conditioning',
'conditions',
'conference',
'confidence',
'confident',
'confirm',
'confirmed',
'confused',
'congrats',
'congratulations',
'congress',
'connection',
'conservative',
'conservatives',
'consider',
'considerate',
...]
In [10]:
import matplotlib;
%matplotlib inline
pd.Series(list(vector.vocabulary_.values()))
Out[10]:
0 4330
1 1531
2 2967
3 3689
4 4644
5 1240
6 1175
7 954
8 2212
9 1982
10 3023
11 4438
12 1714
13 1261
14 4876
15 4067
16 2170
17 602
18 1768
19 3909
20 2141
21 1557
22 791
23 4820
24 1520
25 1239
26 441
27 4576
28 2654
29 2038
...
4970 4737
4971 4006
4972 3326
4973 1697
4974 3511
4975 2690
4976 1502
4977 161
4978 4417
4979 4993
4980 4349
4981 3829
4982 2325
4983 3399
4984 2371
4985 3
4986 4518
4987 4266
4988 1918
4989 2833
4990 1583
4991 1659
4992 2532
4993 4719
4994 593
4995 1724
4996 4783
4997 388
4998 3984
4999 2382
dtype: int64
In [11]:
pd.Series(list(vector.vocabulary_.values())).describe()
# matplotlib.pyplot.plot(pd.Series(list(vector.vocabulary_.values())))
Out[11]:
count 5000.000000
mean 2499.500000
std 1443.520003
min 0.000000
25% 1249.750000
50% 2499.500000
75% 3749.250000
max 4999.000000
dtype: float64
Combine tweets and output to csv
In [12]:
master = sarcastic_tweets.append(regular_tweets)
master.to_csv('all_tweets_df.csv')
Bigram exploratory analysis
In [12]:
from sklearn.feature_extraction.text import CountVectorizer
word_vectorizer = CountVectorizer(ngram_range=(1,2), analyzer='word')
sparse_matrix = word_vectorizer.fit_transform(sarcastic_tweets['0'].values)
frequencies = sum(sparse_matrix).toarray()[0]
pd.DataFrame(frequencies, index=word_vectorizer.get_feature_names(), columns=['frequency'])
Out[12]:
frequency
00
110
00 am
33
00 brilliant
1
00 but
16
00 est
1
00 for
6
00 houston
8
00 if
1
00 in
1
00 it
6
00 pm
5
00 sarcasm
4
00 they
2
00 this
3
00 to
6
00 yay
6
00 youre
6
000
152
000 00
2
000 000
4
000 759
5
000 americans
1
000 and
6
000 back
3
000 bb16
2
000 before
2
000 bucks
3
000 can
1
000 ch14
1
000 copies
3
...
...
zuma
8
zuma beach
1
zuma handling
1
zuma what
6
zumarek
4
zumarek erratarob
4
zumba
5
zuniga
12
zuniga gets
5
zuniga tackle
7
zup
3
zup guys
3
zusi
15
zusi he
7
zusi to
1
zusi walked
7
zwebackhd
2
zynga
5
zyrtec
1
zyrtec med
1
zzxx
4
zzxx make
3
zzz
2
zzz dammit
2
zzzz
7
zzzz haha
2
zzzz wackypeople
5
zzzzz
5
zzzzz is
1
zzzzzzz
7
291505 rows × 1 columns
Composite unigram counts
In [27]:
word_vectorizer = CountVectorizer(ngram_range=(1,1), analyzer='word')
sparse_matrix = word_vectorizer.fit_transform(master['0'].values)
frequencies = sum(sparse_matrix).toarray()[0]
# freqs = pd.DataFrame(frequencies, index=word_vectorizer.get_feature_names(), columns=['frequency'])
Out[27]:
frequency
00
423
000
358
0000
3
00000
1
0000000
1
00000kt
15
00001
1
000052736
1
000094
1
0000ff
1
0002
1
00094
1
000h
1
000s
3
000th
12
000tps
1
000x
3
001
1
0012
1
00136
1
0014priya
1
002
1
003
4
004
1
0049
1
007
3
007_og
1
007hertzrumble
1
0083
1
00979
1
...
...
zwgman
1
zwiebel
1
zwirner
1
zwood93
1
zxabys
1
zxvaness
3
zyadkahtani
1
zymaster
1
zynga
6
zyrtec
1
zysecurity
1
zywievc
1
zz
1
zzaaammnnnn
1
zzquil
1
zzxx
4
zzyzx
1
zzyzzx
2
zzz
6
zzzentropy
2
zzzquil
1
zzzrgrizz
1
zzzxoz
1
zzzz
13
zzzzbruh
1
zzzzz
8
zzzzzz
3
zzzzzzz
8
zzzzzzzz
3
zzzzzzzzz
2
224940 rows × 1 columns
Top 250 Most Frequent Unigrams
In [35]:
freqs = pd.DataFrame(frequencies, index=word_vectorizer.get_feature_names(), columns=['frequency'])
freqs.sort_values('frequency',ascending=False)[:250]
Out[35]:
frequency
the
140144
ud83d
126990
to
115725
you
83257
and
67892
my
66012
is
59652
it
58650
of
55084
in
54773
ud83c
52834
that
50654
for
46944
me
42852
on
38786
so
36209
this
36096
at
31918
be
29981
with
29534
just
29497
like
26915
ude02
25695
have
24618
can
23659
all
22831
are
21366
not
21319
was
21065
love
21011
...
...
days
3100
stop
3096
watching
3094
lmao
3093
miss
3092
follow
3086
other
3077
mean
3070
long
3061
little
3051
having
3037
talk
3017
show
2999
makes
2982
birthday
2973
hope
2965
any
2956
glad
2956
nyc
2956
same
2929
udc95
2913
doing
2907
big
2891
4th
2877
play
2860
person
2857
house
2849
does
2849
haha
2844
funny
2838
250 rows × 1 columns
Content source: thisisbasil/SarcasmDetectionTwitter
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