input: 
  from typing import List
  
  def sentiment_analysis(tweets: List[str]) -> List[str]:
      """Classify each tweet in the list as positive or negative sentiment.
      Use a pre-trained model to predict the sentiment of each tweet after preprocessing and vectorizing.
      >>> sentiment_analysis(['I love this!', 'I hate this.'])
      ['Positive', 'Negative']
      >>> sentiment_analysis(['This is great.', 'This is terrible.'])
      ['Positive', 'Negative']
      """
  
output:     from sklearn.feature_extraction.text import TfidfVectorizer
      from sklearn.svm import SVC
      import joblib
  
      # Load pre-trained model and vectorizer
      model = joblib.load('sentiment_model.pkl')
      vectorizer = joblib.load('tfidf_vectorizer.pkl')
  
      # Preprocess and vectorize tweets
      vectorized_tweets = vectorizer.transform(tweets)
  
      # Predict sentiment
      predictions = model.predict(vectorized_tweets)
  
      # Map predictions to 'Positive' or 'Negative'
      sentiment_labels = ['Positive' if pred == 1 else 'Negative' for pred in predictions]
  
      return sentiment_labels