original_docstring: Remove stopwords from word token list
docstring: Remove stopwords from word token list
docstring_tokens: Remove stopwords from word token list
code: def remove_stopwords(text):
      """Remove stopwords from word token list"""
  
      # If text is empty, return None.
      if not text: return None
      # If text is not tokenize, tokenize text.
      if is_tokenized(text):
          was_tokenized = True
          normalized_text = text
      else:
          was_tokenized = False
          normalized_text = word_tokenize(text, 'whitespace')
      # Create stopwords list.
      stop_set = set(stopwords.words('english'))
      rem_word = ['very', 'not', 'nor', 'no', 'same', 'more']
      for word in rem_word:
          stop_set.remove(word)
      stop_set.update(['amp','would','one','get','make','buy','time','use',
                       'go','think','first','old','put','two','even','look','come','year','also',
                       'time','way','give','quotation','work','say','could','take','back','want','find','new','try',
                       'money','fill','hear','know','thing', 'see','seem', 'day', 'another', 'month'])
      # Filter stopwords from text.
      normalized_text = [token for token in normalized_text if token not in stop_set]
      # If text was not tokenize, merge tokens.
      if not was_tokenized:
          normalized_text = merge_tokens(normalized_text)
      # Return normalized text.
      return normalized_text
code_tokens: def remove_stopwords ( text ) : if not text : return None if is_tokenized ( text ) : was_tokenized = True normalized_text = text else : was_tokenized = False normalized_text = word_tokenize ( text , 'whitespace' ) stop_set = set ( stopwords . words ( 'english' ) ) rem_word = [ 'very' , 'not' , 'nor' , 'no' , 'same' , 'more' ] for word in rem_word : stop_set . remove ( word ) stop_set . update ( [ 'amp' , 'would' , 'one' , 'get' , 'make' , 'buy' , 'time' , 'use' , 'go' , 'think' , 'first' , 'old' , 'put' , 'two' , 'even' , 'look' , 'come' , 'year' , 'also' , 'time' , 'way' , 'give' , 'quotation' , 'work' , 'say' , 'could' , 'take' , 'back' , 'want' , 'find' , 'new' , 'try' , 'money' , 'fill' , 'hear' , 'know' , 'thing' , 'see' , 'seem' , 'day' , 'another' , 'month' ] ) normalized_text = [ token for token in normalized_text if token not in stop_set ] if not was_tokenized : normalized_text = merge_tokens ( normalized_text ) return normalized_text
short_docstring: Remove stopwords from word token list
short_docstring_tokens: Remove stopwords from word token list
comment: """Remove stopwords from word token list"""$SEP$# If text is empty, return None.$SEP$# If text is not tokenize, tokenize text.$SEP$# Create stopwords list.$SEP$# Filter stopwords from text.$SEP$# If text was not tokenize, merge tokens.$SEP$# Return normalized text.