input: 
  import nltk
  from nltk.tokenize import sent_tokenize
  from nltk.classify import NaiveBayesClassifier
  
  def analyze_sentiment(text):
      """Analyze the sentiment of the given text and categorize it into positive, negative, or neutral sentiments.
      >>> analyze_sentiment("I love this product. It's amazing!")
      'positive'
      >>> analyze_sentiment("I hate this product. It's horrible.")
      'negative'
      >>> analyze_sentiment("I feel indifferent about this product.")
      'neutral'
      """
  
  
  Well, now let me ask you that: which data type does the function `analyze_sentiment` return?
output:     sentences = sent_tokenize(text)
      positive_words = ['love', 'amazing', 'great', 'excellent', 'good']
      negative_words = ['hate', 'horrible', 'bad', 'terrible', 'awful']
      
      positive_count = sum(1 for word in nltk.word_tokenize(text.lower()) if word in positive_words)
      negative_count = sum(1 for word in nltk.word_tokenize(text.lower()) if word in negative_words)
      
      if positive_count > negative_count:
          return 'positive'
      elif negative_count > positive_count:
          return 'negative'
      else:
          return 'neutral'