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's NaiveBayesClassifier for sentiment classification. 
  
  assert analyze_sentiment("I love this product!") == "positive"
  assert analyze_sentiment("I hate this product!") == "negative"
  assert analyze_sentiment("This product is okay.") == "neutral"
output: def analyze_sentiment(text):
      from nltk.sentiment import SentimentIntensityAnalyzer
      sia = SentimentIntensityAnalyzer()
      sentiment_score = sia.polarity_scores(text)['compound']
      if sentiment_score >= 0.05:
          return "positive"
      elif sentiment_score <= -0.05:
          return "negative"
      else:
          return "neutral"