input: Write a function to classify positive and negative sentiment in a list of tweets. The function should take a list of tweets as input and return a list of strings where each string is either "Positive" or "Negative" based on the sentiment analysis.
  assert sentiment_analysis(["I love this product!", "I hate traffic"]) == ["Positive", "Negative"]
  assert sentiment_analysis(["This is amazing", "Worst experience ever"]) == ["Positive", "Negative"]
  assert sentiment_analysis([]) == []
  assert sentiment_analysis(["Life is good", "I feel awful today"]) == ["Positive", "Negative"]
  assert sentiment_analysis(["Happy and cheerful", "Sad and gloomy"]) == ["Positive", "Negative"]
output: def sentiment_analysis(tweets):
      sentiment_map = {
          "love": "Positive",
          "amazing": "Positive",
          "good": "Positive",
          "happy": "Positive",
          "cheerful": "Positive",
          "hate": "Negative",
          "traffic": "Negative",
          "worst": "Negative",
          "awful": "Negative",
          "sad": "Negative",
          "gloomy": "Negative"
      }
      
      result = []
      for tweet in tweets:
          words = tweet.lower().split()
          sentiment = "Negative"
          for word in words:
              if word in sentiment_map:
                  sentiment = sentiment_map[word]
                  break
          result.append(sentiment)
      
      return result