input: Write a function to perform sentiment analysis on product reviews using natural language processing techniques like tokenization, stopword removal, lemmatization, and n-gram generation, and then use a classification model to assign sentiments. The function should read a CSV file of reviews, preprocess the text, generate features, and train a Naive Bayes classifier to predict sentiments. The function should return the classification report containing precision, recall, and F1-score.
  
  assert sentiment_analysis('reviews.csv') == '''              precision    recall  f1-score   support
  
            0       0.89      0.92      0.90       100
            1       0.95      0.92      0.93       150
  
  avg / total       0.92      0.92      0.92       250'''
output: 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
  import nltk
  from nltk.corpus import stopwords
  from nltk.tokenize import word_tokenize
  from nltk.stem import WordNetLemmatizer
  
  nltk.download('punkt')
  nltk.download('stopwords')
  nltk.download('wordnet')
  
  def sentiment_analysis(file_path):
      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.isalnum() and word not in stop_words]
          return ' '.join(tokens)
  
      df['processed_text'] = df['review'].apply(preprocess)
      X = df['processed_text']
      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, output_dict=True)
      return classification_report(y_test, y_pred)