input: import nltk
  from nltk.tokenize import sent_tokenize
  from nltk.classify import NaiveBayesClassifier
  
  def analyze_sentiment(text):
      sents = sent_tokenize(text)
      neg = 0
      pos = 0
      for sentence in sents:
          sentiment_analysis = NaiveBayesClassifier.classify(sentence)
          if sentiment_analysis == 'neg':
              neg += 1
          if sentiment_analysis == 'pos':
              pos += 1
      
      if neg > pos:
          sentiment = 'negative'
      elif pos > neg:
          sentiment = 'positive'
      else:
          sentiment = 'neutral'
      
      return sentiment
  
  # End analyze sentiment definition
  
  # Example usage
  text1 = "I love this product. It's the best I've ever used."
  print(analyze_sentiment(text1))  # Output: positive
  
  text2 = "I hate this product. It's terrible."
  print(analyze_sentiment(text2))  # Output: negative
  
  text3 = "The product is okay, but nothing special."
  print(analyze_sentiment(text3))  # Output: neutral
output:     from nltk.sentiment import SentimentIntensityAnalyzer
  
      sia = SentimentIntensityAnalyzer()
  
      for sentence in sents:
          sentiment_scores = sia.polarity_scores(sentence)
          if sentiment_scores['compound'] < 0:
              neg += 1
          elif sentiment_scores['compound'] > 0:
              pos += 1