input: Write a function to classify text documents into two classes: positive and negative using the NaiveBayesClassifier from the NLTK library. The function should take a text string as input and return either 'positive' or 'negative'. If the input text is empty, the function should return an empty string.
  import nltk
  from nltk.classify import NaiveBayesClassifier 
    
  def label_words(words): 
      return dict([(word, True) for word in words]) 
  
  positiveText = "This is a great movie!"
  negativeText = "This movie was terrible"
  
  positive_tokens = nltk.word_tokenize(positiveText) 
  negative_tokens = nltk.word_tokenize(negativeText)
  
  positive_data = [(label_words(positive_tokens), 'positive')] 
  negative_data = [(label_words(negative_tokens), 'negative')]
   
  train_data = positive_data + negative_data 
  classifier = NaiveBayesClassifier.train(train_data)
  
  def classify_text(text):
      if not text:
          return ""
      return classifier.classify(label_words(nltk.word_tokenize(text)))
  
  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'
  assert classify_text("")==""
output: def classify_text(text):
      if not text:
          return ""
      return classifier.classify(label_words(nltk.word_tokenize(text)))