input: 
  import nltk
  from nltk.tokenize import sent_tokenize
  from nltk.classify import NaiveBayesClassifier
  
  def analyze_sentiment(text):
      """ Analyzes the sentiment of a given text and categorizes it into
      positive, negative, or neutral sentiments. The text is tokenized into
      sentences, and each sentence is classified using a NaiveBayesClassifier.
      The overall sentiment of the text is determined by comparing the number
      of positive and negative classifications.
      
      >>> analyze_sentiment("I love this product! It's amazing!")
      'positive'
      >>> analyze_sentiment("I hate this product. It's terrible.")
      'negative'
      >>> analyze_sentiment("This product is ok.")
      '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
  
output:     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