input: def Sentiment_Analysis(review_file: str) -> str:
      """Perform sentiment analysis on product reviews using natural language processing techniques.
      The program will tokenize the reviews, remove stopwords, normalize the words, and generate
      features using CountVectorizer. A Naive Bayes classifier will be trained on the data and
      used to assign sentiments to the reviews. A classification report will be printed as
      the output.
  
      >>> Sentiment_Analysis('reviews.csv')
      '              precision    recall  f1-score   support\n\n           0       0.88      0.91      0.89       100\n           1       0.92      0.89      0.90       120\n\n    accuracy                           0.90       220\n   macro avg       0.90      0.90      0.90       220\nweighted avg       0.90      0.90      0.90       220\n'
      """
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
      from nltk.corpus import stopwords
      from nltk.tokenize import word_tokenize
      import string
  
      # Load the dataset
      df = pd.read_csv(review_file)
  
      # Preprocessing
      def preprocess(text):
          tokens = word_tokenize(text.lower())
          tokens = [word for word in tokens if word.isalpha()]
          tokens = [word for word in tokens if word not in stopwords.words('english')]
          return ' '.join(tokens)
  
      df['review'] = df['review'].apply(preprocess)
  
      # Feature extraction
      vectorizer = CountVectorizer()
      X = vectorizer.fit_transform(df['review'])
      y = df['sentiment']
  
      # Train-test split
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      # Train the model
      model = MultinomialNB()
      model.fit(X_train, y_train)
  
      # Predict and evaluate
      y_pred = model.predict(X_test)
      report = classification_report(y_test, y_pred)
  
      return report