input: Write a function to classify text documents into two classes: positive and negative using the Naive Bayes classifier from the NLTK library. The function should take a text string as input and return the classification (either 'positive' or 'negative').
  assert classify_text("This is a great movie!")=='positive'
  assert classify_text("This movie was terrible")=='negative'
  assert classify_text("This movie was ok")=='positive'
output: def classify_text(text):
      from nltk.classify import NaiveBayesClassifier
      from nltk.corpus import movie_reviews
      from nltk.tokenize import word_tokenize
  
      def extract_features(words):
          return dict([(word, True) for word in words])
  
      positive_reviews = [(extract_features(word_tokenize(movie_reviews.raw(fileid))), 'positive') for fileid in movie_reviews.fileids('pos')]
      negative_reviews = [(extract_features(word_tokenize(movie_reviews.raw(fileid))), 'negative') for fileid in movie_reviews.fileids('neg')]
  
      train_set = positive_reviews + negative_reviews
      classifier = NaiveBayesClassifier.train(train_set)
  
      return classifier.classify(extract_features(word_tokenize(text)))