input: Write a function to categorize the sentiment of given text into positive, negative, and neutral sentiments using a NaiveBayesClassifier from the NLTK library. The function should tokenize the text into sentences, analyze the sentiment of each sentence, and return the overall sentiment of the text based on the majority classification. If the given text is empty, return 'neutral'.
  
  
  def analyze_sentiment(text):
      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
  
  assert analyze_sentiment("I love this product") == 'positive'
  assert analyze_sentiment("I hate this service") == 'negative'
  assert analyze_sentiment("The weather is okay today") == 'neutral'
  assert analyze_sentiment("") == 'neutral'
  
output: def analyze_sentiment(text):
      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