Summary of the paper

Title Distribution of Emotional Reactions to News Articles in Twitter
Authors Omar Juárez Gambino, Hiram Calvo and Consuelo-Varinia García-Mendoza
Abstract 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.
Topics Opinion Mining / Sentiment Analysis, Emotion Recognition/Generation, Corpus (Creation, Annotation, Etc.)
Full paper Distribution of Emotional Reactions to News Articles in Twitter
Bibtex @InProceedings{GAMBINO18.416,
  author = {Omar Juárez Gambino and Hiram Calvo and Consuelo-Varinia García-Mendoza},
  title = "{Distribution of Emotional Reactions to News Articles in Twitter}",
  booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)},
  year = {2018},
  month = {May 7-12, 2018},
  address = {Miyazaki, Japan},
  editor = {Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Hélène Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis and Takenobu Tokunaga},
  publisher = {European Language Resources Association (ELRA)},
  isbn = {979-10-95546-00-9},
  language = {english}
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