input: from typing import List
  
  def classify_text(texts: List[str]) -> List[str]:
      """
      Given a list of text documents, this function classifies each document as either 'positive' or 'negative' using a NaiveBayesClassifier.
      
      >>> classify_text(["This is a great movie!", "This movie was terrible"])
      ['positive', 'negative']
      """
      
      positiveText = "This is a great movie!"
      negativeText = "This movie was terrible"
      
      import nltk
      from nltk.classify import NaiveBayesClassifier 
      
      def label_words(words): 
          return dict([(word, True) for word in words]) 
      
      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:
          classification = classifier.classify(label_words(nltk.word_tokenize(text)))
          results.append(classification)
      
      return results
output:     positiveText = "This is a great movie!"
      negativeText = "This movie was terrible"
      
      import nltk
      from nltk.classify import NaiveBayesClassifier 
      
      def label_words(words): 
          return dict([(word, True) for word in words]) 
      
      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:
          classification = classifier.classify(label_words(nltk.word_tokenize(text)))
          results.append(classification)
      
      return results