Emotional reactions prediction of news posts

Nowadays, on-line news agents post news articles on social media platforms with the aim to attract more users. Different types of news trigger different emotions on users who may feel surprised or sad after reading some piece of news. In this paper, we are interested in predicting the amount of emotional reactions triggered on users after reading a news post. To address the problem, we propose a model that is trained on features extracted from users' early commenting activity. Our results show that users' early activity features are very important and that combining those features with terms can effectively predict the amount of emotional reactions triggered on users by a news post.