Predicting emotional reactions to news articles in social networks. (November 2019) - Gambino, Omar Juárez; Calvo, Hiram
Highlights: Novel method for predicting the distribution of emotions to be expected in a social network after the publication of a news article. We consider not only bags of words, but we also experiment with word embeddings. We believe that our approach to this problem is new, because previous research has focused only on the presence or absence of an emotion in a nonpsychological motivated set of emotions. Our model accounts for the amount of each emotion that could be present not individually, but in a whole group of users. Annotation scheme that can be used at different degrees of coarseness. Abstract: After reading a news article, some readers post their opinion to social networks, particularly as tweets. These opinions (responses) have an important emotional content. By analyzing users' responses in context, it is possible to find a set of emotions expressed in these tweets. In this work we propose a method to predict the emotional reactions that Twitter users would have after reading a news article. We consider the prediction of emotions as a classification problem and we follow a supervised approach. For this purpose, we collected a corpus of Spanish news articles and their associated tweet responses. Then, a group of annotators tagged the emotions expressed in them. Twitter users can express more than one emotion in their responses, so that in this work we deal with this characteristic by using a multi-target classification strategy. The use of this strategy allows an instance (a news article) to have more than one associated class (emotions expressed by users). In addition to that, the multi-target strategy permits to predict not only the emotional reactions, but also the intensity of these emotions, considering how often each specific emotion was triggered by users. By measuring the deviation of the predicted emotional reactions with regard to the annotated ones, we obtain an emotional reactions similarity of 89%.