docs: def clean_twitter_list(twitter_list,
                         sent_tokenize, _treebank_word_tokenize,
                         tagger, lemmatizer, lemmatize, stopset,
                         first_cap_re, all_cap_re, digits_punctuation_whitespace_re,
                         pos_set):
      """
      Extracts the *set* of keywords found in a Twitter list (name + description).
  
      Inputs: - twitter_list: A Twitter list in json format.
              - lemmatizing: A string containing one of the following: "porter", "snowball" or "wordnet".
  
      Output: - keyword_set: A set of keywords (i.e. not a bag-of-words) in python set format.
              - lemma_to_keywordbag: A python dictionary that maps stems/lemmas to original topic keywords.
      """
      name_lemmas, name_lemma_to_keywordbag = clean_document(twitter_list["name"].replace("_", " ").replace("-", " "),
                                                             sent_tokenize, _treebank_word_tokenize,
                                                             tagger, lemmatizer, lemmatize, stopset,
                                                             first_cap_re, all_cap_re, digits_punctuation_whitespace_re,
                                                             pos_set)
      description_lemmas, description_lemma_to_keywordbag = clean_document(twitter_list["description"].replace("_", " ").replace("-", " "),
                                                             sent_tokenize, _treebank_word_tokenize,
                                                             tagger, lemmatizer, lemmatize, stopset,
                                                             first_cap_re, all_cap_re, digits_punctuation_whitespace_re,
                                                             pos_set)
  
      keyword_set = set(name_lemmas + description_lemmas)
  
      lemma_to_keywordbag = defaultdict(lambda: defaultdict(int))
      for lemma, keywordbag in name_lemma_to_keywordbag.items():
          for keyword, multiplicity in keywordbag.items():
              lemma_to_keywordbag[lemma][keyword] += multiplicity
  
      for lemma, keywordbag in description_lemma_to_keywordbag.items():
          for keyword, multiplicity in keywordbag.items():
              lemma_to_keywordbag[lemma][keyword] += multiplicity
  
      return keyword_set, lemma_to_keywordbag