input: 
  def classify_string(input_string):
      """
      This function takes a user input string and categorizes it using a machine learning classifier.
      The classifier used is a Multinomial Naive Bayes classifier from the scikit-learn library.
      
      >>> classify_string("This is a positive comment")
      1
      >>> classify_string("This is a negative comment")
      0
      """
      # 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.naive_bayes import MultinomialNB
  
      # Example data for training
      data = ["This is a positive comment", "This is a negative comment"]
      target = [1, 0]
  
      # Feature extraction and transformation from string to numeric
      vectorizer = CountVectorizer()
      features = vectorizer.fit_transform(data).toarray()
  
      # Create an instance of the classifier 
      model = MultinomialNB()
      
      # Train the classifier on the data
      model.fit(features, target)
      
      # Transform the input string to numeric features
      input_features = vectorizer.transform([input_string]).toarray()
      
      # Make a prediction based on the input
      prediction = model.predict(input_features)
      
      # Return the prediction
      return prediction[0]
  