Towards building a social emotion detection system for online news

J. Lei, Y. Rao, Qing Li, X. Quan, L. Wenyin

Research output: Journal article publicationJournal articleAcademic researchpeer-review

35 Citations (Scopus)

Abstract

Social emotion detection of online users has become an important task for mining public opinions. Social emotion detection aims at predicting the readers' emotions evoked by news articles, tweets, etc. In this article, we focus on building a social emotion detection system for online news. The system is built based on the modules of document selection, Part-of-speech (POS) tagging, and social emotion lexicon generation. Empirical studies are extensively conducted on a large scale real-world collection of news articles. Experiments show that the document selection algorithm has a positive effect on the social emotion detection. The system performs better with the words and POS combination compared to a feature set consisting only of words. POS is also useful to detect emotion ambiguity of words and the context dependence of their sentiment orientations. Furthermore, the proposed method of generating the lexicon outperforms the baselines in terms of social emotion prediction. © 2013 Elsevier B.V. All rights reserved.
Original languageEnglish
Pages (from-to)438-448
Number of pages11
JournalFuture Generation Computer Systems
Volume37
DOIs
Publication statusPublished - 1 Jan 2014
Externally publishedYes

Keywords

  • Emotion lexicon
  • Part of speech
  • Social emotion detection

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Networks and Communications

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