original_docstring:  Computes similarity score of corpus characterization and input
          tweets.
          
          Args:
          tweets (1D numpy array): plaintext tweets. 
              
          Returns:
          1D numpy array: similarity scores by tweet supplied.
          
docstring: Computes similarity score of corpus characterization and input
  tweets.
  
  tweets (1D numpy array): plaintext tweets.
  
  1D numpy array: similarity scores by tweet supplied.
docstring_tokens: Computes similarity score of corpus characterization and input tweets . tweets ( 1D numpy array ) : plaintext tweets . 1D numpy array : similarity scores by tweet supplied .
code: def similarity_score(self, tweets):
          """ Computes similarity score of corpus characterization and input
          tweets.
          
          Args:
          tweets (1D numpy array): plaintext tweets. 
              
          Returns:
          1D numpy array: similarity scores by tweet supplied.
          """
          
          tfidf_vectorizer = TfidfVectorizer(stop_words='english', use_idf=False,
                                             vocabulary = self.feature_names)
          
          tweet_freqs = tfidf_vectorizer.fit_transform(tweets)
          
          return tweet_freqs.dot(self.word_freq_vec)
code_tokens: def similarity_score ( self , tweets ) : tfidf_vectorizer = TfidfVectorizer ( stop_words = 'english' , use_idf = False , vocabulary = self . feature_names ) tweet_freqs = tfidf_vectorizer . fit_transform ( tweets ) return tweet_freqs . dot ( self . word_freq_vec )
short_docstring: Computes similarity score of corpus characterization and input
  tweets.
short_docstring_tokens: Computes similarity score of corpus characterization and input tweets .
comment: """ Computes similarity score of corpus characterization and input
          tweets.
          
          Args:
          tweets (1D numpy array): plaintext tweets. 
              
          Returns:
          1D numpy array: similarity scores by tweet supplied.
          """