input: from typing import List
  
  def analyze_sentiment(text: str) -> str:
      """Analyzes the sentiment of the given text and categorizes it into positive, negative, or neutral sentiment.
      
      >>> analyze_sentiment("I love this product but it is a bit expensive.")
      'positive'
      >>> analyze_sentiment("I hate this product it is absolutely terrible.")
      'negative'
      >>> analyze_sentiment("This product is neither good nor bad.")
      'neutral'
      """
      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
output:     from nltk.tokenize import sent_tokenize
      from nltk.classify import NaiveBayesClassifier