input: from typing import List
  
  def classify_text(texts: List[str]) -> List[str]:
      """ Classify a list of text documents into two classes: positive and negative.
      >>> 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])
      
      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)
      
      results = []
      for text in texts:
          label = classifier.classify(label_words(nltk.word_tokenize(text)))
          results.append(label)
      
      return results
output:     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)
      
      results = []
      for text in texts:
          label = classifier.classify(label_words(nltk.word_tokenize(text)))
          results.append(label)
      
      return results