original_docstring: 
          main function that recagorizes tweets based on the initial input of tweets
          
docstring: main function that recagorizes tweets based on the initial input of tweets
docstring_tokens: main function that recagorizes tweets based on the initial input of tweets
code: def _recategorize_mast_(self):
          """
          main function that recagorizes tweets based on the initial input of tweets
          """
          # create a copy of the original tweet dict to work with
  #        tweet_dict = self._tweet_dict.copy()
          i = 1
          while True:
              n_changed = 0
              # iterate over all the tweets in the master input dict
              for tweet, value in self._tweet_dict.items():
                  # only check and change the topic if the topic was NOT on the topic of interest
                  if value['topic'] == self._no_topic_label:
                      result = self._recategorize_check_tweet_(tweet)
                      if result == "change":
                          self._tweet_dict[tweet]['topic'] = self._topic_for_which_to_check
                          n_changed += 1
              print("{i} iteration completed, recategorized this round: {n}".format(
                  i=i, n=n_changed))
              i += 1
              # if no more tweets are being changed, exit the loop
              if n_changed == 0:
                  break
          return self._tweet_dict
code_tokens: def _recategorize_mast_ ( self ) : i = 1 while True : n_changed = 0 for tweet , value in self . _tweet_dict . items ( ) : if value [ 'topic' ] == self . _no_topic_label : result = self . _recategorize_check_tweet_ ( tweet ) if result == "change" : self . _tweet_dict [ tweet ] [ 'topic' ] = self . _topic_for_which_to_check n_changed += 1 print ( "{i} iteration completed, recategorized this round: {n}" . format ( i = i , n = n_changed ) ) i += 1 if n_changed == 0 : break return self . _tweet_dict
short_docstring: main function that recagorizes tweets based on the initial input of tweets
short_docstring_tokens: main function that recagorizes tweets based on the initial input of tweets
comment: """
          main function that recagorizes tweets based on the initial input of tweets
          """$SEP$# create a copy of the original tweet dict to work with$SEP$#        tweet_dict = self._tweet_dict.copy()$SEP$# iterate over all the tweets in the master input dict$SEP$# only check and change the topic if the topic was NOT on the topic of interest$SEP$# if no more tweets are being changed, exit the loop