input: def Sentiment_Analysis():
      """Create a Python program to perform sentiment analysis on product reviews. 
      The program should use natural language processing techniques like tokenization and n-grams to generate features for sentiment analysis and use a classification model to assign sentiments to the reviews.
      Example:
      for Sentiment_Analysis() == '              precision    recall  f1-score   support\n\n           0       0.88      0.92      0.90       100\n           1       0.92      0.88      0.90       100\n\n    accuracy                           0.90       200\n   macro avg       0.90      0.90      0.90       200\nweighted avg       0.90      0.90      0.90       200\n'
      """
output:     import pandas as pd
      from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.model_selection import train_test_split
      from sklearn.naive_bayes import MultinomialNB
      from sklearn.metrics import classification_report
  
      # Sample data
      data = {
          'review': [
              'This product is amazing!',
              'I hate this product.',
              'Great value for money.',
              'Terrible experience.',
              'Love it!',
              'Worst purchase ever.',
              'Highly recommended.',
              'Not worth the price.',
              'Excellent quality.',
              'Disappointed with the product.'
          ],
          'sentiment': [1, 0, 1, 0, 1, 0, 1, 0, 1, 0]
      }
  
      df = pd.DataFrame(data)
  
      # Tokenization and n-grams
      vectorizer = CountVectorizer(ngram_range=(1, 2))
      X = vectorizer.fit_transform(df['review'])
      y = df['sentiment']
  
      # Split data
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      # Train model
      model = MultinomialNB()
      model.fit(X_train, y_train)
  
      # Predict and evaluate
      y_pred = model.predict(X_test)
      report = classification_report(y_test, y_pred)
  
      return report