input: Write a function to implement sentiment analysis on product reviews using natural language processing techniques such as tokenization, n-grams, and classification modeling. The function should read a dataset of reviews, preprocess the texts, generate features using CountVectorizer, and train a Multinomial Naive Bayes model to classify the sentiment of the reviews. Finally, it should output a classification report.
  
  assert sentiment_analysis_program('reviews.csv')==classification_report
  assert sentiment_analysis_program('empty_reviews.csv')==classification_report
output: def sentiment_analysis_program(file_path):
      import pandas as pd
      from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.naive_bayes import MultinomialNB
      from sklearn.model_selection import train_test_split
      from sklearn.metrics import classification_report
  
      df = pd.read_csv(file_path)
      if df.empty:
          return classification_report([])
  
      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)
  
      y_pred = model.predict(X_test_vec)
      return classification_report(y_test, y_pred)