input: Write a function to perform sentiment analysis on product reviews using natural language processing techniques like tokenization, removal of stopwords, lemmatization, and n-grams, and using a Multinomial Naive Bayes model to classify reviews as either positive or negative. The function should read a CSV file containing reviews, process the reviews, train a classification model, and generate a classification report.
  
  assert sentiment_analysis("reviews.csv")==print(classification_report(y_test, predictions))
  assert sentiment_analysis("another_reviews.csv")==print(classification_report(y_test, predictions))
  assert sentiment_analysis("empty.csv")==None
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
  from nltk.corpus import stopwords
  from nltk.tokenize import word_tokenize
  from nltk.stem import WordNetLemmatizer
  import string
  
  def sentiment_analysis(file_path):
      try:
          df = pd.read_csv(file_path)
      except pd.errors.EmptyDataError:
          return None
      
      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)
      
      X_train, X_test, y_train, y_test = train_test_split(df['processed_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)
      
      predictions = model.predict(X_test_vec)
      return print(classification_report(y_test, predictions))
  