input: Write a function to classify the sentiment of tweets as either "Positive" or "Negative". The function will take a list of tweets and return a list of sentiment classifications.
  assert sentiment_analysis(["I love this product!", "This is terrible!"])==["Positive", "Negative"]
  assert sentiment_analysis(["Awesome!", "Horrible experience."])==["Positive", "Negative"]
  assert sentiment_analysis(["Great service", "The worst"])==["Positive", "Negative"]
  assert sentiment_analysis(["Happy with the results", "Disappointing"])==["Positive", "Negative"]
  assert sentiment_analysis([])==[]
output: def sentiment_analysis(tweets):
      sentiment_map = {
          "love": "Positive",
          "awesome": "Positive",
          "great": "Positive",
          "happy": "Positive",
          "terrible": "Negative",
          "horrible": "Negative",
          "worst": "Negative",
          "disappointing": "Negative"
      }
      result = []
      for tweet in tweets:
          words = tweet.lower().split()
          for word in words:
              if word in sentiment_map:
                  result.append(sentiment_map[word])
                  break
      return result