input: Write a function to classify positive and negative sentiment in tweets. The function takes a list of tweets as input, cleans each tweet, vectorizes it, and then predicts the sentiment. The function returns a list of sentiment labels. If the prediction is 1, it labels the sentiment as "Positive", otherwise it labels it as "Negative".
  def sentiment_analysis(tweets):
      # Initialize a list to store the result 
      results = [] 
  
      # Iterate through each tweet
      for tweet in tweets:
          # Clean the tweet 
          tweet = preprocessing(tweet)
  
          # Vectorize the tweet and obtain the prediction 
          tweet_vector = vectorize(tweet)
          prediction = predict_sentiment(tweet_vector)
  
          # Append the prediction to the result list
          if prediction == 1:
              results.append("Positive")
          elif prediction == 0:
              results.append("Negative")
  
      return results
  
  assert sentiment_analysis(["I love this!", "This is terrible."])==["Positive", "Negative"]
  assert sentiment_analysis(["The weather is great!", "I feel so sad today."])==["Positive", "Negative"]
  assert sentiment_analysis(["Amazing day!", "So disappointing."])==["Positive", "Negative"]
  assert sentiment_analysis(["Happy times.", "Worst experience ever."])==["Positive", "Negative"]
  assert sentiment_analysis([])==[]
output: def sentiment_analysis(tweets):
      results = [] 
      for tweet in tweets:
          tweet = preprocessing(tweet)
          tweet_vector = vectorize(tweet)
          prediction = predict_sentiment(tweet_vector)
          if prediction == 1:
              results.append("Positive")
          elif prediction == 0:
              results.append("Negative")
      return results