input: Write a function to perform sentiment analysis on product reviews using natural language processing techniques like tokenization, n-grams, and a classification model to assign sentiments to the reviews. The function should handle the entire process from reading the data to printing the classification report.
  assert perform_sentiment_analysis("reviews.csv")
  assert perform_sentiment_analysis("empty_reviews.csv")
  assert perform_sentiment_analysis("mixed_sentiment_reviews.csv")
  assert perform_sentiment_analysis("positive_reviews.csv")
  assert perform_sentiment_analysis("negative_reviews.csv")
output: def perform_sentiment_analysis(file_path):
      import pandas as pd
      from sklearn.feature_extraction.text import TfidfVectorizer
      from sklearn.model_selection import train_test_split
      from sklearn.naive_bayes import MultinomialNB
      from sklearn.metrics import classification_report
  
      # Load the data
      df = pd.read_csv(file_path)
      
      # Check if the file is empty
      if df.empty:
          return
  
      # Preprocess the data
      df['review'] = df['review'].str.lower()
      df['review'] = df['review'].str.replace('[^\w\s]', '')
  
      # Split the data
      X_train, X_test, y_train, y_test = train_test_split(df['review'], df['sentiment'], test_size=0.2, random_state=42)
  
      # Vectorize the text data
      vectorizer = TfidfVectorizer(ngram_range=(1, 2))
      X_train_vec = vectorizer.fit_transform(X_train)
      X_test_vec = vectorizer.transform(X_test)
  
      # Train the model
      model = MultinomialNB()
      model.fit(X_train_vec, y_train)
  
      # Predict and print the classification report
      y_pred = model.predict(X_test_vec)
      print(classification_report(y_test, y_pred))