input: Write a function to create a Python program that performs sentiment analysis on product reviews. The program should use natural language processing techniques like tokenization, stopwords removal, lemmatization, and n-grams to generate features for sentiment analysis. It should then use a classification model to assign sentiments to the reviews. The program should read data from a CSV file named 'reviews.csv', tokenize the reviews, remove stopwords, normalize the words, generate features using CountVectorizer, split the data into training and test sets, train a Multinomial Naive Bayes model, make predictions on the test set, and generate a classification report.
  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
      from nltk.corpus import stopwords
      from nltk.tokenize import word_tokenize
      from nltk.stem import WordNetLemmatizer
      import string
  
      df = pd.read_csv(file_path)
      stop_words = set(stopwords.words('english'))
      lemmatizer = WordNetLemmatizer()
  
      def preprocess(text):
          tokens = word_tokenize(text.lower())
          tokens = [lemmatizer.lemmatize(word) for word in tokens if word.isalpha() and word not in stop_words]
          return ' '.join(tokens)
  
      df['processed_review'] = df['review'].apply(preprocess)
  
      vectorizer = CountVectorizer(ngram_range=(1, 2))
      X = vectorizer.fit_transform(df['processed_review'])
      y = df['sentiment']
  
      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