TY - GEN
T1 - Distribution of emotional reactions to news articles in twitter
AU - Gambino, Omar Juárez
AU - Calvo, Hiram
AU - García-Mendoza, Consuelo Varinia
N1 - Publisher Copyright:
© LREC 2018 - 11th International Conference on Language Resources and Evaluation. All rights reserved.
PY - 2019
Y1 - 2019
N2 - Several datasets of opinions expressed by Social networks' users have been created to explore Sentiment Analysis tasks like Sentiment Polarity and Emotion Mining. Most of these datasets are focused on the writers' perspective, that is, the post written by a user is analyzed to determine the expressed sentiment on it. This kind of datasets do not consider the source that provokes those opinions (e.g. a previous post). In this work, we propose a dataset focused on the readers' perspective. The developed dataset contains news articles published by three newspapers and the distribution of six predefined emotions expressed by readers of the articles in Twitter. This dataset was built aiming to explore how the six emotions are expressed by Twitter users' after reading a news article. We show some results of a machine learning method used to predict the distribution of emotions in unseen news articles.
AB - Several datasets of opinions expressed by Social networks' users have been created to explore Sentiment Analysis tasks like Sentiment Polarity and Emotion Mining. Most of these datasets are focused on the writers' perspective, that is, the post written by a user is analyzed to determine the expressed sentiment on it. This kind of datasets do not consider the source that provokes those opinions (e.g. a previous post). In this work, we propose a dataset focused on the readers' perspective. The developed dataset contains news articles published by three newspapers and the distribution of six predefined emotions expressed by readers of the articles in Twitter. This dataset was built aiming to explore how the six emotions are expressed by Twitter users' after reading a news article. We show some results of a machine learning method used to predict the distribution of emotions in unseen news articles.
KW - Emotion distribution
KW - Reader's emotions
KW - Twitter Sentiment Analysis
UR - http://www.scopus.com/inward/record.url?scp=85059912568&partnerID=8YFLogxK
M3 - Contribución a la conferencia
AN - SCOPUS:85059912568
T3 - LREC 2018 - 11th International Conference on Language Resources and Evaluation
SP - 1419
EP - 1424
BT - LREC 2018 - 11th International Conference on Language Resources and Evaluation
A2 - Isahara, Hitoshi
A2 - Maegaard, Bente
A2 - Piperidis, Stelios
A2 - Cieri, Christopher
A2 - Declerck, Thierry
A2 - Hasida, Koiti
A2 - Mazo, Helene
A2 - Choukri, Khalid
A2 - Goggi, Sara
A2 - Mariani, Joseph
A2 - Moreno, Asuncion
A2 - Calzolari, Nicoletta
A2 - Odijk, Jan
A2 - Tokunaga, Takenobu
PB - European Language Resources Association (ELRA)
T2 - 11th International Conference on Language Resources and Evaluation, LREC 2018
Y2 - 7 May 2018 through 12 May 2018
ER -