Early Commenting Features for Emotional Reactions Prediction
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Nowadays, one of the main sources for people to access and read news are social media platforms. Different types of news trigger different emotional reactions to users who may feel happy or sad after reading a news article. In this paper, we focus on the problem of predicting emotional reactions that are triggered on users after they read a news post. In particular, we try to predict the number of emotional reactions that users express regarding a news post that is published on social media. In this paper, we propose features extracted from users’ comments published about a news post shortly after its publication to predict users’ the triggered emotional reactions. We explore two different sets of features extracted from users’ comments. The first group represents the activity of users in publishing comments whereas the second refers to the comments’ content. In addition, we combine the features extracted from the comments with textual features extracted from the news post. Our results show that features extracted from users’ comments are very important for the emotional reactions prediction of news posts and that combining textual and commenting features can effectively address the problem of emotional reactions prediction.
This paper was partially funded by the Swiss National Science Foundation (SNSF) under the project OpiTrack.
The work of the second author was partially funded by the the Spanish MINECO under the research project SomEMBED (TIN2015-71147-C2-1-P).
This paper is partially supported by the BIGDATAGRAPES project (grant agreement N. 780751) that received funding from the European Union’s Horizon 2020 research and innovation programme under the Information and Communication Technologies programme.
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