input: 
  from sklearn.naive_bayes import MultinomialNB 
  
  def classify_string(input_string):
      """
      Classifies an input string using the Multinomial Naive Bayes classifier.
      >>> classify_string("I love reading books.")
      [0]  # Assuming 0 corresponds to the category 'Positive'
      >>> classify_string("I hate waiting in lines.")
      [1]  # Assuming 1 corresponds to the category 'Negative'
      """
      # Feature extraction and transformation from string to numeric
      features = ...
  
      # Create an instance of the classifier 
      model = MultinomialNB()
  
      # Train the classifier on the data
      model.fit(data, target)
  
      # Make a prediction based on the input
      prediction = model.predict([features])
  
      # Return the prediction
      return prediction
  
output:     from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.model_selection import train_test_split
      from sklearn.datasets import fetch_20newsgroups
  
      # Load dataset
      categories = ['alt.atheism', 'soc.religion.christian']
      newsgroups_train = fetch_20newsgroups(subset='train', categories=categories)
      data = newsgroups_train.data
      target = newsgroups_train.target
  
      # Feature extraction and transformation from string to numeric
      vectorizer = CountVectorizer()
      X = vectorizer.fit_transform(data)
      features = vectorizer.transform([input_string])
  
      # Create an instance of the classifier 
      model = MultinomialNB()
  
      # Train the classifier on the data
      model.fit(X, target)
  
      # Make a prediction based on the input
      prediction = model.predict(features)
  
      # Return the prediction
      return prediction