input: from typing import List
  
  def classify_text(texts: List[str]) -> List[str]:
      """ Given a list of text documents, classify each document into two classes:
      positive or negative using Naive Bayes Classifier.
      >>> classify_text(["This is a great movie!", "This movie was terrible"])
      ['positive', 'negative']
      """
      import nltk
      from nltk.classify import NaiveBayesClassifier
      
      def label_words(words): 
          return dict([(word, True) for word in words]) 
  
      positive_text = "This is a great movie!"
      negative_text = "This movie was terrible"
  
      positive_tokens = nltk.word_tokenize(positive_text) 
      negative_tokens = nltk.word_tokenize(negative_text)
  
      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)
  
      results = []
      for text in texts:
          tokens = nltk.word_tokenize(text)
          classified = classifier.classify(label_words(tokens))
          results.append(classified)
      
      return results
output:     import nltk
      from nltk.classify import NaiveBayesClassifier
      
      def label_words(words): 
          return dict([(word, True) for word in words]) 
  
      positive_text = "This is a great movie!"
      negative_text = "This movie was terrible"
  
      positive_tokens = nltk.word_tokenize(positive_text) 
      negative_tokens = nltk.word_tokenize(negative_text)
  
      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)
  
      results = []
      for text in texts:
          tokens = nltk.word_tokenize(text)
          classified = classifier.classify(label_words(tokens))
          results.append(classified)
      
      return results