input: 
  from sklearn.naive_bayes import MultinomialNB
  
  def classify_string(input_string):
      """ Given an input string, this function extracts features from the string,
      trains a Naive Bayes classifier on a dataset, and then predicts the category
      of the input string. The prediction is then returned as the output.
      >>> classify_string("positive sentiment")
      ['positive']
      >>> classify_string("negative sentiment")
      ['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
      import numpy as np
  
      # Example dataset
      data = ["positive sentiment", "negative sentiment", "neutral sentiment", "positive sentiment", "negative sentiment"]
      target = ["positive", "negative", "neutral", "positive", "negative"]
  
      # Feature extraction and transformation from string to numeric
      vectorizer = CountVectorizer()
      X = vectorizer.fit_transform(data)
      features = vectorizer.transform([input_string])
  
      # Split data into training and testing sets
      X_train, _, y_train, _ = train_test_split(X, target, test_size=0.2, random_state=42)
  
      # Create an instance of the classifier 
      model = MultinomialNB()
      
      # Train the classifier on the data
      model.fit(X_train, y_train)
      
      # Make a prediction based on the input
      prediction = model.predict(features)
      
      # Return the prediction
      return prediction