input: Write a function to analyze the sentiment of given text using a Naive Bayes classifier from the nltk library. The function should categorize the text into positive, negative, and neutral sentiments based on the classification of its sentences. If the majority of the sentences are classified as negative, the function should return 'negative'. If the majority are positive, it should return 'positive'. If the number of positive and negative sentences is equal, it should return 'neutral'. If the text is empty, it should also return 'neutral'.
  assert analyze_sentiment("The weather is nice today.") == 'positive'
  assert analyze_sentiment("I do not like the movie.") == 'negative'
  assert analyze_sentiment("It is a typical day.") == 'neutral'
  assert analyze_sentiment("") == 'neutral'
output: def analyze_sentiment(text):
      if not text:
          return 'neutral'
      
      from nltk.sentiment import SentimentIntensityAnalyzer
      sia = SentimentIntensityAnalyzer()
      
      sentences = text.split('.')
      positive_count = 0
      negative_count = 0
      
      for sentence in sentences:
          if sentence.strip():
              sentiment = sia.polarity_scores(sentence)
              if sentiment['compound'] > 0:
                  positive_count += 1
              elif sentiment['compound'] < 0:
                  negative_count += 1
      
      if positive_count > negative_count:
          return 'positive'
      elif negative_count > positive_count:
          return 'negative'
      else:
          return 'neutral'