input: Write a function to perform sentiment analysis on product reviews using natural language processing techniques like tokenization and n-grams, and classify the sentiment using a Multinomial Naive Bayes model. The function should read reviews from a CSV file, preprocess the text, generate features, train a model, and output a classification report.
  assert sentiment_analysis_program(pd.DataFrame({
      'review': ['This product is amazing!', 'I hate this product.'],
      'sentiment': ['positive', 'negative']
  }))==classification_report(y_test, predictions)
  assert sentiment_analysis_program(pd.DataFrame({
      'review': ['This product is okay.', 'I love this product!', 'This is the worst product ever.'],
      'sentiment': ['neutral', 'positive', 'negative']
  }))==classification_report(y_test, predictions)
  assert sentiment_analysis_program(pd.DataFrame({
      'review': ['The product is very good.', 'I am not impressed.', 'The best product I have ever used.'],
      'sentiment': ['positive', 'negative', 'positive']
  }))==classification_report(y_test, predictions)
output: 
  def sentiment_analysis_program(df):
      from sklearn.model_selection import train_test_split
      from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.naive_bayes import MultinomialNB
      from sklearn.metrics import classification_report
      import pandas as pd
  
      X = df['review']
      y = df['sentiment']
  
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      vectorizer = CountVectorizer(ngram_range=(1, 2))
      X_train_vec = vectorizer.fit_transform(X_train)
      X_test_vec = vectorizer.transform(X_test)
  
      model = MultinomialNB()
      model.fit(X_train_vec, y_train)
  
      predictions = model.predict(X_test_vec)
      return classification_report(y_test, predictions)
  