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feature_eng_cleaned.py
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feature_eng_cleaned.py
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# -*- coding: utf-8 -*-
"""
Created on Sat Sep 1 18:45:29 2018
@author: Gurunath
"""
#from data_analysis import tweet_df
import flashtext
import re
import spacy
from sklearn.feature_extraction.text import TfidfVectorizer
import pandas as pd
from nltk import word_tokenize
from nltk.corpus import stopwords
import string
from textblob import TextBlob
from sklearn.feature_extraction.text import CountVectorizer
import pickle
kp=flashtext.KeywordProcessor()
kp.add_keyword('$&@*#')
kp.add_keyword('fuck')
kp.add_keyword('fucking')
kp.add_keyword('sucks')
kp.add_keyword('ass')
kp.get_all_keywords()
def extract_urls(x):
try:
res=re.search("(?P<url>https?://[^\s]+)",x).group("url")
except:
pass
return ''
return res
def UrlPresence(X):
if X!=None:
return 1
else:
return 0
def offensive_words_presence(X):
if len(X)!=0:
return 1
else:
return 0
def encode_pos_neg(x):
if x.classification=='pos':
return 0
else:
return 1
def clean_tweet(x):
# w_list=word_tokenize(x)
# table = str.maketrans(" ",string.punctuation)
pattern = re.compile(r'\b(' + r'|'.join(stopwords.words('english')) + r')\b\s*')
text = pattern.sub('', x)
# text=text.translate(table, string.punctuation)
text=''.join(ch for ch in text if ch not in string.punctuation)
text=''.join(ch for ch in text if not ch.isdigit())
# pattern = re.compile(r'\b(' + r'|'+string.punctuation + r')\b\s*')
# text = pattern.sub('', str(x))
return text
def textblob_sentiment(x):
sent=TextBlob(x)
return sent.sentiment #[sent.subjectivity,sent.polarity]
def create_features(tweets,tfidf,off_words_cv,url_cv,
do_text_clean=False,need_tfidf=True):
df=pd.DataFrame()
df['tweet']=tweets
df['no_of_words']=df['tweet'].apply(lambda x :len(x.split(' ')))
df['offensive_words']=df.tweet.apply(lambda x :kp.extract_keywords(x))
df['offensive_words']=df['offensive_words'].apply(lambda x :','.join(x))
df['offwords_presence']=df['offensive_words']\
.apply(offensive_words_presence)
off_words_mat=off_words_cv.transform(df['offensive_words']).toarray()
# print(off_words_mat)
off_words_df=pd.DataFrame(data=off_words_mat,columns=off_words_cv.get_feature_names())
df['urls']=df['tweet'].apply(extract_urls)
df['url_presence']=df['urls'].apply(UrlPresence)
url_words_mat=url_cv.transform(df['urls']).toarray()
url_words_df=pd.DataFrame(data=url_words_mat,columns=url_cv.get_feature_names())
print(url_words_df)
df.drop(['urls','offensive_words'],axis=1,inplace=True)
print(df.columns)
sentiment=df['tweet'].apply(textblob_sentiment)
blob_df=pd.DataFrame(columns=['subjectivity','polarity'])
blob_df['subjectivity']=sentiment.apply(lambda x:x.subjectivity)
blob_df['polarity']=sentiment.apply(lambda x:x.polarity)
# sentiment=df['tweet'].apply(textblob_sentiment_with_analyzer)
# blob_df['class']=sentiment.apply(encode_pos_neg)
# blob_df['p_neg']=sentiment.apply(lambda x:x.p_neg)
# blob_df['p_pos']=sentiment.apply(lambda x:x.p_pos)
if do_text_clean:
tweets=df['tweet'].apply(clean_tweet)
else:
tweets=df['tweet']
if need_tfidf:
tfidf_df=pd.DataFrame(tfidf.transform(tweets).toarray())
res=pd.concat([off_words_df,url_words_df,blob_df,tfidf_df],axis=1)
else:
res=pd.concat([off_words_df,url_words_df,blob_df],axis=1)
return res
def fit_tfidf(tweets,name):
tfidf=TfidfVectorizer(stop_words='english')
vect=tfidf.fit_transform(tweets)
with open(r'F:\E\Learning_DL_fastai\competition\NLP_data\model_files\{}.pkl'.format(name),'wb') as f:
pickle.dump(tfidf,f)
return tfidf,vect.toarray()
if __name__=='__main__':
tweet_df=pd.read_csv(r'F:\E\Learning_DL_fastai\competition\NLP_data\train_2kmZucJ.csv')
tfidf,_=fit_tfidf(tweet_df['tweet'],'complete_text')
off_words_cv=CountVectorizer()
off_words_cv.fit(list(kp.get_all_keywords().keys()))
url_cv=CountVectorizer()
url_cv.fit(['fb','instagr','google','bit','tmblr','youtu','goo','ebay'])
sdf=create_features(tweet_df['tweet'],tfidf,off_words_cv,url_cv)
sdf.to_csv('fe_check.csv')