input: Write a function to perform sentiment analysis on product reviews. The function should use natural language processing techniques like tokenization, removing stopwords, lemmatization, and n-gram generation to create features for sentiment analysis, and use a classification model (e.g., Multinomial Naive Bayes) to predict the sentiment of the reviews. The function should return a classification report summarizing the model's performance on the test set.
  assert sentiment_analysis('reviews.csv') is not None
  assert isinstance(sentiment_analysis('reviews.csv'), str)
  assert 'precision' in sentiment_analysis('reviews.csv')
  assert 'recall' in sentiment_analysis('reviews.csv')
  assert 'f1-score' in sentiment_analysis('reviews.csv')
output: 
  import pandas as pd
  from sklearn.model_selection import train_test_split
  from sklearn.feature_extraction.text import CountVectorizer
  from sklearn.naive_bayes import MultinomialNB
  from sklearn.metrics import classification_report
  import nltk
  from nltk.corpus import stopwords
  from nltk.stem import WordNetLemmatizer
  import re
  
  nltk.download('stopwords')
  nltk.download('wordnet')
  
  def sentiment_analysis(file_path):
      df = pd.read_csv(file_path)
      df['review'] = df['review'].apply(lambda x: re.sub(r'\W', ' ', x))
      stop_words = set(stopwords.words('english'))
      lemmatizer = WordNetLemmatizer()
      
      def preprocess(text):
          tokens = nltk.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['review'] = df['review'].apply(preprocess)
      X_train, X_test, y_train, y_test = train_test_split(df['review'], df['sentiment'], 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)
  