docs: def user_twitter_list_bag_of_words(twitter_list_corpus,
                                     sent_tokenize, _treebank_word_tokenize,
                                     tagger, lemmatizer, lemmatize, stopset,
                                     first_cap_re, all_cap_re, digits_punctuation_whitespace_re,
                                     pos_set):
      """
      Extract a bag-of-words for a corpus of Twitter lists pertaining to a Twitter user.
  
      Inputs: - twitter_list_corpus: A python list of Twitter lists in json format.
              - lemmatizing: A string containing one of the following: "porter", "snowball" or "wordnet".
  
      Output: - bag_of_words: A bag-of-words in python dictionary format.
              - lemma_to_keywordbag_total: Aggregated python dictionary that maps stems/lemmas to original topic keywords.
      """
      # Extract a bag-of-words from a list of Twitter lists.
      # May result in empty sets
      list_of_keyword_sets, list_of_lemma_to_keywordbags = clean_list_of_twitter_list(twitter_list_corpus,
                                                                                      sent_tokenize, _treebank_word_tokenize,
                                                                                      tagger, lemmatizer, lemmatize, stopset,
                                                                                      first_cap_re, all_cap_re, digits_punctuation_whitespace_re,
                                                                                      pos_set)
  
      # Reduce keyword sets.
      bag_of_words = reduce_list_of_bags_of_words(list_of_keyword_sets)
  
      # Reduce lemma to keywordbag maps.
      lemma_to_keywordbag_total = defaultdict(lambda: defaultdict(int))
      for lemma_to_keywordbag in list_of_lemma_to_keywordbags:
          for lemma, keywordbag in lemma_to_keywordbag.items():
              for keyword, multiplicity in keywordbag.items():
                  lemma_to_keywordbag_total[lemma][keyword] += multiplicity
  
      return bag_of_words, lemma_to_keywordbag_total