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. If the given text is empty then return 'neutral'.
  assert analyze_sentiment("I love this product. It's amazing!") == 'positive'
  assert analyze_sentiment("I hate this product. It's terrible!") == 'negative'
  assert analyze_sentiment("This product is okay.") == 'neutral'
  assert analyze_sentiment("") == 'neutral'
output: def analyze_sentiment(text):
      if not text:
          return 'neutral'
      from nltk.sentiment import SentimentIntensityAnalyzer
      sia = SentimentIntensityAnalyzer()
      sentiment_scores = sia.polarity_scores(text)
      if sentiment_scores['compound'] >= 0.05:
          return 'positive'
      elif sentiment_scores['compound'] <= -0.05:
          return 'negative'
      else:
          return 'neutral'