input: from typing import List
  
  def analyze_sentiment(text: str) -> str:
      """Analyze the sentiment of the given text and categorize it into positive, negative, or neutral.
      
      >>> analyze_sentiment("I absolutely loved the movie, it was fantastic!")
      'positive'
      >>> analyze_sentiment("I hate rainy days, they are so depressing.")
      'negative'
      >>> analyze_sentiment("It is a sunny day today.")
      '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