sentences.py

corpus = [sent.text.lower() for sent in doc.sents ]
cv = CountVectorizer(stop_words=list(STOP_WORDS))   
cv_fit=cv.fit_transform(corpus)    
word_list = cv.get_feature_names();    
count_list = cv_fit.toarray().sum(axis=0)    

"""The zip(*iterables) function takes iterables as arguments and returns an iterator. 
This iterator generates a series of tuples containing elements from each iterable. 
Let's convert these tuples to {word:frequency} dictionary"""

word_frequency = dict(zip(word_list,count_list))