input: Write a function to classify text documents into two classes: positive and negative using the NaiveBayesClassifier from nltk. If the text is positive, return 'positive'; if the text is negative, return 'negative'. Make sure to preprocess the text by tokenizing it and labeling the words with a boolean value.
  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.tokenize import word_tokenize
  
      def create_word_features(words):
          useful_words = [word for word in words if word.isalnum()]
          my_dict = dict([(word, True) for word in useful_words])
          return my_dict
  
      positive_vocab = ['great', 'awesome', 'good', 'excellent', 'amazing', 'ok']
      negative_vocab = ['bad', 'terrible', 'awful', 'horrible', 'worst']
  
      positive_features = [(create_word_features(word_tokenize(pos)), 'positive') for pos in positive_vocab]
      negative_features = [(create_word_features(word_tokenize(neg)), 'negative') for neg in negative_vocab]
  
      train_set = positive_features + negative_features
      classifier = NaiveBayesClassifier.train(train_set)
  
      words = word_tokenize(text.lower())
      words = create_word_features(words)
      return classifier.classify(words)