input: Write a function to analyze the sentiment of given text and categorize it into positive, negative, and neutral sentiments. The function should use NLTK for tokenization and a Naive Bayes Classifier for sentiment classification. Return the sentiment category that has the majority count. If the counts are equal, return 'neutral'.
  
  assert analyze_sentiment("I love this movie! It's amazing.") == 'positive'
  assert analyze_sentiment("This movie is terrible. I hate it.") == 'negative'
  assert analyze_sentiment("This movie is just okay. Nothing special.") == 'neutral'
  assert analyze_sentiment("") == 'neutral'
  assert analyze_sentiment("I feel good today. But I hate the weather.") == 'neutral'
output: def analyze_sentiment(text):
      from nltk.tokenize import word_tokenize
      from nltk.sentiment import SentimentIntensityAnalyzer
  
      sia = SentimentIntensityAnalyzer()
      tokens = word_tokenize(text)
      sentiment_scores = [sia.polarity_scores(token)['compound'] for token in tokens]
      
      positive_count = sum(1 for score in sentiment_scores if score > 0)
      negative_count = sum(1 for score in sentiment_scores if score < 0)
      neutral_count = sum(1 for score in sentiment_scores if score == 0)
      
      if positive_count > negative_count and positive_count > neutral_count:
          return 'positive'
      elif negative_count > positive_count and negative_count > neutral_count:
          return 'negative'
      else:
          return 'neutral'