input: def analyze_sentiment(text: str) -> str:
      """ Analyzes the sentiment of a given text and categorizes it into positive, negative, or neutral sentiments.
      Uses NaiveBayesClassifier from nltk.
      >>> analyze_sentiment("I love the weather today.")
      'positive'
      >>> analyze_sentiment("I dislike the rain.")
      'negative'
      >>> analyze_sentiment("Today is a day.")
      'neutral'
      """
      sents = sent_tokenize(text)
      neg = 0
      pos = 0
      for sentence in sents:
          sentiment_analysis = NaiveBayesClassifier.classify(sentence)
          if sentiment_analysis == 'neg':
              neg += 1
          if sentiment_analysis == 'pos':
              pos += 1
      
      if neg > pos:
          sentiment = 'negative'
      elif pos > neg:
          sentiment = 'positive'
      else:
          sentiment = 'neutral'
      
      return sentiment
  
  # End analyze sentiment definition
output:     from nltk.tokenize import sent_tokenize
      from nltk.classify import NaiveBayesClassifier
  
      # Assuming NaiveBayesClassifier is trained and available
      # Example training code:
      # from nltk.corpus import subjectivity
      # from nltk.sentiment import SentimentAnalyzer
      # from nltk.sentiment.util import *
      # nltk.download('subjectivity')
      # n_instances = 100
      # subj_docs = [(sent, 'subj') for sent in subjectivity.sents(categories='subj')[:n_instances]]
      # obj_docs = [(sent, 'obj') for sent in subjectivity.sents(categories='obj')[:n_instances]]
      # train_data = subj_docs + obj_docs
      # sentim_analyzer = SentimentAnalyzer()
      # all_words_neg = sentim_analyzer.all_words([mark_negation(doc) for doc in train_data])
      # unigram_feats = sentim_analyzer.unigram_word_feats(all_words_neg, min_freq=4)
      # sentim_analyzer.add_feat_extractor(extract_unigram_feats, unigrams=unigram_feats)
      # training_set = sentim_analyzer.apply_features(train_data)
      # trainer = NaiveBayesClassifier.train
      # classifier = sentim_analyzer.train(trainer, training_set)
      # NaiveBayesClassifier.train(training_set)