input: Write a function to perform sentiment analysis on product reviews using natural language processing techniques like tokenization and n-grams, and use a classification model to assign sentiments to the reviews. The function should read the reviews from a CSV file, preprocess the text, generate features using CountVectorizer, split the data into training and test sets, train a Multinomial Naive Bayes classifier, and print a classification report based on the test data.
  assert perform_sentiment_analysis('reviews.csv') is not None
  assert "classification report" in perform_sentiment_analysis('reviews.csv')
output: def perform_sentiment_analysis(file_path):
      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
  
      df = pd.read_csv(file_path)
      reviews = df['review']
      sentiments = df['sentiment']
  
      vectorizer = CountVectorizer(ngram_range=(1, 2))
      X = vectorizer.fit_transform(reviews)
  
      X_train, X_test, y_train, y_test = train_test_split(X, sentiments, test_size=0.2, random_state=42)
  
      classifier = MultinomialNB()
      classifier.fit(X_train, y_train)
  
      y_pred = classifier.predict(X_test)
      print(classification_report(y_test, y_pred))