input: Write a function to analyze the sentiment of given text and categorize it into positive, negative, and neutral sentiments using Naive Bayes Classifier. If the text has more negative sentences than positive, return "negative". If the text has more positive sentences than negative, return "positive". If the number of positive and negative sentences is equal, return "neutral".
  assert analyze_sentiment("I hate this.") == "negative"
  assert analyze_sentiment("I love this.") == "positive"
  assert analyze_sentiment("This is okay.") == "neutral"
  assert analyze_sentiment("I hate this. But I love this.") == "neutral"
  assert analyze_sentiment("Not bad, but not good.") == "neutral"
output: def analyze_sentiment(text):
      from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.naive_bayes import MultinomialNB
      import nltk
      nltk.download('punkt')
      from nltk.tokenize import sent_tokenize
  
      sentences = sent_tokenize(text)
      vectorizer = CountVectorizer()
      X = vectorizer.fit_transform(sentences)
  
      clf = MultinomialNB()
      clf.fit([[1], [1], [1]], ["negative", "positive", "neutral"])
  
      predictions = clf.predict(X)
      positive_count = sum(1 for p in predictions if p == "positive")
      negative_count = sum(1 for p in predictions if p == "negative")
  
      if positive_count > negative_count:
          return "positive"
      elif negative_count > positive_count:
          return "negative"
      else:
          return "neutral"