Modeling distribution of emotional reactions in social media using a multi-target strategy
Issue title: Intelligent and Fuzzy Systems applied to Language & Knowledge Engineering
Guest editors: David Pinto, Vivek Kumar Singh, Aline Villavicencio, Philipp Mayr-Schlegel and Efstathios Stamatatos
Article type: Research Article
Authors: Gambino, Omar Juáreza; b; * | Calvo, Hiramb
Affiliations: [a] Instituto Politécnico Nacional – ESCOM, Lindavista, G.A. Madero, Mexico City, Mexico | [b] Instituto Politécnico Nacional – CIC, J.D. Bátiz e/M.O. de Mendizábal, Mexico City, Mexico
Correspondence: [*] Corresponding author. Omar Juárez Gambino. E-mail: b150697@sagitario.cic.ipn.mx.
Abstract: Social networks users often post their opinion after reading a news article. By analyzing these responses, it is possible to find diverse emotions expressed in them. When several users react to an article, a distribution of these emotions is accumulated. Writers and publishers would benefit to have an estimation of how users will react to an article. This work proposes a method to predict the distribution of emotions that users would express in Twitter after reading a news article. More than one emotion can be expressed in responses, so that an approach of modeling this distribution as a supervised multi-target classification problem is followed. For this purpose, it was necessary to collect a corpus of Spanish news articles and their associated responses and a group of annotators tagged the emotions expressed in them. The use of this strategy allows to naturally model instances (news articles) that have more than one associated class (emotions expressed in responses). The predicted values are expressed in terms of the percentage of responses that triggered each specific emotion. The proposed method is evaluated by measuring the deviation of the predicted emotion distribution with regard to the annotated set of emotions, obtaining a precision above 90%. In addition to that, the proposed method was used in a foreign corpus in order to compare it with 10 state of the art methods. Results show that the proposed method performs better than 9 of these methods on this corpus.
Keywords: Social media emotion reaction, Twitter sentiment analysis, emotion distribution prediction, multi-target classification
DOI: 10.3233/JIFS-169471
Journal: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 5, pp. 2837-2847, 2018
Prediction of emotional reaction of social media users to news articles
What is it about?
Social networks users express emotions in their post as a reaction to news articles. By using a supervised algorithm, we proposed a method to predict the distribution of the emotions that users will express as a reaction to news articles.
Why is it important?
Emotion detection has been mostly studied from the writer's perspective where the content of a post is used to determine the sentiment polarity or emotions expressed on it. On the other hand, there has been less efforts in detecting the emotion from the reader’s perspective, where the content of a post is used to determine the sentiment polarity or emotions that this post would generate on readers. In addition to that, to our knowledge the use of a multi-target strategy to predict multiple emotions has not been used before.