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 language | English |
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| Title of host publication | Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation, PACLIC 2016 |
| Publisher | Institute for the Study of Language and Information |
| Pages | 153-162 |
| Number of pages | 10 |
| ISBN (Electronic) | 9788968174285 |
| Publication status | Published - 1 Jan 2016 |
| Event | 30th Pacific Asia Conference on Language, Information and Computation, PACLIC 2016 - Kyung Hee University, Seoul, Korea, Republic of Duration: 28 Oct 2016 → 30 Oct 2016 |
Conference
| Conference | 30th Pacific Asia Conference on Language, Information and Computation, PACLIC 2016 |
|---|---|
| Country/Territory | Korea, Republic of |
| City | Seoul |
| Period | 28/10/16 → 30/10/16 |
ASJC Scopus subject areas
- Language and Linguistics
- Computer Science (miscellaneous)
- Information Systems