input: Write a python function to classify text as positive or negative using NaiveBayesClassifier from nltk library.
  assert classify_text("This movie was ok") == 'positive'
  assert classify_text("I did not enjoy this movie at all") == 'negative'
  assert classify_text("What a fantastic experience!") == 'positive'
output: def classify_text(text):
      import nltk
      from nltk.classify import NaiveBayesClassifier
      from nltk.corpus import movie_reviews
      import random
  
      def extract_features(words):
          return {word: True for word in words}
      
      # Load movie reviews dataset
      nltk.download('movie_reviews')
      documents = [(list(movie_reviews.words(fileid)), category)
                   for category in movie_reviews.categories()
                   for fileid in movie_reviews.fileids(category)]
      random.shuffle(documents)
      
      # Extract features from the dataset
      featuresets = [(extract_features(d), c) for (d, c) in documents]
      train_set, test_set = featuresets[:1900], featuresets[1900:]
      
      # Train the NaiveBayesClassifier
      classifier = NaiveBayesClassifier.train(train_set)
      
      # Classify the input text
      features = extract_features(text.split())
      return classifier.classify(features)