input: Write a function to classify text documents into two classes: positive and negative using Naive Bayes classification. The function should take a text as input and return a classification as "positive" or "negative". The training data consists of two sample texts, one positive and one negative.
  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):
      tokens = nltk.word_tokenize(text)
      result = classifier.classify(label_words(tokens))
      return result
  
  assert classify_text("This movie was ok") == "positive"
  assert classify_text("This movie was awful") == "negative"
  assert classify_text("This was the best movie ever!") == "positive"
  assert classify_text("I hated every minute of it") == "negative"
  assert classify_text("It was decent") == "positive"
output: def classify_text(text):
      tokens = nltk.word_tokenize(text)
      result = classifier.classify(label_words(tokens))
      return result