input: from typing import List, Dict
  
  def analyze_sentiment(reviews: List[str]) -> Dict[str, str]:
      """ Perform sentiment analysis on the given list of product reviews. The analysis 
      involves tokenizing the reviews, eliminating stopwords, normalizing the words, 
      and generating features using n-grams. After that, a classification model is used 
      to predict the sentiment of each review which can be positive, negative, or neutral.
      The function returns a dictionary where the key is the review text and the value is 
      the predicted sentiment.
      >>> analyze_sentiment(["Great product, I love it!", "Terrible experience, would not 
      recommend"])
      {'Great product, I love it!': 'positive', 'Terrible experience, would not 
      recommend': 'negative'}
      """
      
output:     from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.naive_bayes import MultinomialNB
      from sklearn.pipeline import make_pipeline
      from nltk.corpus import stopwords
      from nltk.tokenize import word_tokenize
      from nltk.stem import WordNetLemmatizer
      import string
  
      def preprocess(text):
          tokens = word_tokenize(text.lower())
          tokens = [token for token in tokens if token not in stopwords.words('english') and token not in string.punctuation]
          lemmatizer = WordNetLemmatizer()
          tokens = [lemmatizer.lemmatize(token) for token in tokens]
          return ' '.join(tokens)
  
      reviews = [preprocess(review) for review in reviews]
  
      model = make_pipeline(CountVectorizer(ngram_range=(1, 2)), MultinomialNB())
      model.fit([review for review in reviews], ['positive', 'negative'])
  
      sentiments = model.predict(reviews)
      return {review: sentiment for review, sentiment in zip(reviews, sentiments)}