input: Write a function to classify user input using a machine learning classifier in Python. The function should use a Naive Bayes model and assume that the input string is passed as a parameter. The function should return the prediction based on the input.
  
  from sklearn.naive_bayes import MultinomialNB
  
  def classify_string(input_string):
      # 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
  
  assert classify_string("example input")==whatever_category_you_expect
  assert classify_string("another input")==different_category_you_expect
  assert classify_string("neutral input")==neutral_category_you_expect
output: def classify_string(input_string):
      from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.naive_bayes import MultinomialNB
      import joblib
      
      # Load pre-trained model and vectorizer
      model = joblib.load('naive_bayes_model.pkl')
      vectorizer = joblib.load('count_vectorizer.pkl')
      
      # Transform input string to numeric features
      features = vectorizer.transform([input_string])
      
      # Make a prediction based on the input
      prediction = model.predict(features)
      
      # Return the prediction
      return prediction[0]