input: Write a function to create a Python program that performs sentiment analysis on product reviews using natural language processing techniques like tokenization and n-grams. The program should generate features for sentiment analysis and use a classification model to assign sentiments to the reviews.
  
  assert perform_sentiment_analysis("reviews.csv")==classification_report
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)
      X = df['review']
      y = df['sentiment']
  
      vectorizer = CountVectorizer(ngram_range=(1, 2))
      X = vectorizer.fit_transform(X)
  
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      model = MultinomialNB()
      model.fit(X_train, y_train)
  
      y_pred = model.predict(X_test)
      report = classification_report(y_test, y_pred)
  
      return report