Tweets_mix = []
for i, row in enumerate(sentence_snowstemmeed):
    all_row = ""
    for sent in row:
        all_row += sent
    Tweets_mix.append(all_row)

tfidf_model = TfidfVectorizer(max_df=0.8, max_features=1000,
                              min_df=0.2, stop_words='english',
                              use_idf=True, tokenizer=None, ngram_range=(1,1))

tfidf_matrix = tfidf_model.fit_transform(Tweets_mix) #fit the vectorizer to synopses

print ("In total, there are " + str(tfidf_matrix.shape[0]) + \
      " synoposes and " + str(tfidf_matrix.shape[1]) + " terms.")