Event based emotion classification for news articles

Minglei Li, Da Wang, Qin Lu, Yunfei Long

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

4 Citations (Scopus)

Abstract

Reading of news articles can trigger emotional reactions from its readers. But comparing to other genre of text, news articles that are mainly used to report events, lack emotion linked words and other features for emotion classification. In this paper, we propose an event anchor based method for emotion classification for news articles. Firstly, we build an emotion linked news corpus through crowdsourcing. Then we propose a CRF based event anchor extraction method to identify event related anchor words that can potentially trigger emotions. These anchor words are then used as features to train a classifier for emotion classification. Experiment shows that our proposed anchor word based method achieves comparable performance to bag-ofword based method and it also performs better than emotion lexicon features. Combining anchor words with bag-of-words can increase the performance by 7.0% under weighted Fscore. Evaluation on the SemEval 2007 news headlines task shows that our method outperforms most of other methods.
Original languageEnglish
Title of host publicationProceedings of the 30th Pacific Asia Conference on Language, Information and Computation, PACLIC 2016
PublisherInstitute for the Study of Language and Information
Pages153-162
Number of pages10
ISBN (Electronic)9788968174285
Publication statusPublished - 1 Jan 2016
Event30th Pacific Asia Conference on Language, Information and Computation, PACLIC 2016 - Kyung Hee University, Seoul, Korea, Republic of
Duration: 28 Oct 201630 Oct 2016

Conference

Conference30th Pacific Asia Conference on Language, Information and Computation, PACLIC 2016
CountryKorea, Republic of
CitySeoul
Period28/10/1630/10/16

ASJC Scopus subject areas

  • Language and Linguistics
  • Computer Science (miscellaneous)
  • Information Systems

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