input: Write a function to categorize the sentiment of given text into positive, negative, and neutral sentiments using the Naive Bayes classifier from NLTK. The function should split the text into sentences, classify each sentence, and then determine the overall sentiment based on the number of positive and negative classifications. If the given text is empty, the function should return 'neutral'.
  
  import nltk
  from nltk.tokenize import sent_tokenize
  from nltk.classify import NaiveBayesClassifier
  
  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
  
  # End analyze sentiment definition
  
  assert analyze_sentiment("I love this movie. The acting was amazing!") == 'positive'
  assert analyze_sentiment("This movie was terrible. The acting was terrible!") == 'negative'
  assert analyze_sentiment("This movie was okay. The acting was decent.") == 'neutral'
  assert analyze_sentiment("") == 'neutral'
output: def analyze_sentiment(text):
      if not text:
          return 'neutral'
      
      sents = sent_tokenize(text)
      neg = 0
      pos = 0
      
      for sentence in sents:
          sentiment_analysis = NaiveBayesClassifier.classify(sentence)
          if sentiment_analysis == 'neg':
              neg += 1
          elif sentiment_analysis == 'pos':
              pos += 1
      
      if neg > pos:
          sentiment = 'negative'
      elif pos > neg:
          sentiment = 'positive'
      else:
          sentiment = 'neutral'
      
      return sentiment