File Download
  Links for fulltext
     (May Require Subscription)
Supplementary

Conference Paper: A topic model for building fine-grained domain-specific emotion lexicon

TitleA topic model for building fine-grained domain-specific emotion lexicon
Authors
Issue Date2014
PublisherAssociation for Computational Linguistics (ACL).
Citation
The 52nd Annual Meeting of the Association for Computational Linguistics (ACL 2014), Baltimore, MD., 22-27 June 2014. In Conference Proceedings, 2014, p. 421-426 How to Cite?
AbstractEmotion lexicons play a crucial role in sentiment analysis and opinion mining. In this paper, we propose a novel Emotion-aware LDA (EaLDA) model to build a domainspecific lexicon for predefined emotions that include anger, disgust, fear, joy, sadness, surprise. The model uses a minimal set of domain-independent seed words as prior knowledge to discover a domainspecific lexicon, learning a fine-grained emotion lexicon much richer and adaptive to a specific domain. By comprehensive experiments, we show that our model can generate a high-quality fine-grained domain-specific emotion lexicon. © 2014 Association for Computational Linguistics.
Persistent Identifierhttp://hdl.handle.net/10722/219240
ISBN

 

DC FieldValueLanguage
dc.contributor.authorYang, M-
dc.contributor.authorPeng, B-
dc.contributor.authorChen, Z-
dc.contributor.authorZhu, D-
dc.contributor.authorChow, KP-
dc.date.accessioned2015-09-18T07:18:35Z-
dc.date.available2015-09-18T07:18:35Z-
dc.date.issued2014-
dc.identifier.citationThe 52nd Annual Meeting of the Association for Computational Linguistics (ACL 2014), Baltimore, MD., 22-27 June 2014. In Conference Proceedings, 2014, p. 421-426-
dc.identifier.isbn978-193728473-2-
dc.identifier.urihttp://hdl.handle.net/10722/219240-
dc.description.abstractEmotion lexicons play a crucial role in sentiment analysis and opinion mining. In this paper, we propose a novel Emotion-aware LDA (EaLDA) model to build a domainspecific lexicon for predefined emotions that include anger, disgust, fear, joy, sadness, surprise. The model uses a minimal set of domain-independent seed words as prior knowledge to discover a domainspecific lexicon, learning a fine-grained emotion lexicon much richer and adaptive to a specific domain. By comprehensive experiments, we show that our model can generate a high-quality fine-grained domain-specific emotion lexicon. © 2014 Association for Computational Linguistics.-
dc.languageeng-
dc.publisherAssociation for Computational Linguistics (ACL).-
dc.relation.ispartofProceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Short Papers)-
dc.rightsProceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Short Papers). © 2014 Association for Computational Linguistics.-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.titleA topic model for building fine-grained domain-specific emotion lexicon-
dc.typeConference_Paper-
dc.identifier.emailChow, KP: kpchow@hkucc.hku.hk-
dc.identifier.authorityChow, KP=rp00111-
dc.description.naturepublished_or_final_version-
dc.identifier.scopuseid_2-s2.0-84906923438-
dc.identifier.hkuros255016-
dc.identifier.spage421-
dc.identifier.epage426-
dc.publisher.placeUnited States-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats