Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

9-2017

Abstract

Depending on the reader, A news article may be viewed from many different perspectives, thus triggering different (and possibly contradicting) emotions. In this paper, we formulate a problem of predicting readers’ emotion distribution affected by a news article. Our approach analyzes affective annotations provided by readers of news articles taken from a non-English online news site. We create a new corpus from the annotated articles, and build a domain-specific emotion lexicon and word embedding features. We finally construct a multi-target regression model from a set of features extracted from online news articles. Our experiments show that by combining lexicon and word embedding features, our regression model is able to predict the emotion distribution with RMSE scores between 0.067 to 0.232 for each emotion category.

Keywords

Social emotion, Multi target regression, Machine learning

Discipline

Social Media | Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

Social informatics: 9th International Conference, SocInfo 2017, Oxford, UK, September 13-15: Proceedings

Volume

10540

First Page

426

Last Page

439

ISBN

9783319672168

Identifier

10.1007/978-3-319-67217-5_26

Publisher

Springer

City or Country

Cham

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1007/978-3-319-67217-5_26

Download

Share

COinS