Inferring Spread of Readers’ Emotion Affected by Online News

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10539)

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 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.School of Information SystemsSingapore Management UniversitySingaporeSingapore
  2. 2.Human Capital CenterPT Telekomunikasi IndonesiaBandungIndonesia

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