TY - JOUR
T1 - Modeling distribution of emotional reactions in social media using a multi-target strategy
AU - Gambino, Omar Juárez
AU - Calvo, Hiram
N1 - Publisher Copyright:
© 2018-IOS Press and the authors. All rights reserved.
PY - 2018
Y1 - 2018
N2 - 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 userswould 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.
AB - 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 userswould 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.
KW - Emotion distribution prediction
KW - Multi-target classification
KW - Social media emotion reaction
KW - Twitter sentiment analysis
UR - http://www.scopus.com/inward/record.url?scp=85059918276&partnerID=8YFLogxK
U2 - 10.3233/JIFS-169471
DO - 10.3233/JIFS-169471
M3 - Artículo
SN - 1064-1246
VL - 34
SP - 2837
EP - 2847
JO - Journal of Intelligent and Fuzzy Systems
JF - Journal of Intelligent and Fuzzy Systems
IS - 5
ER -