Joint multi-grain topic sentiment: modeling semantic aspects for online reviews
DC Field | Value | Language |
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dc.contributor.author | Alam, Md Hijbul | - |
dc.contributor.author | Ryu, Woo-Jong | - |
dc.contributor.author | Lee, SangKeun | - |
dc.date.accessioned | 2021-09-04T00:26:57Z | - |
dc.date.available | 2021-09-04T00:26:57Z | - |
dc.date.created | 2021-06-17 | - |
dc.date.issued | 2016-04-20 | - |
dc.identifier.issn | 0020-0255 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/88905 | - |
dc.description.abstract | The availability of electronic word-of-mouth, online consumer reviews, is increasing rapidly. Users frequently look for important aspects of a product or service in the reviews. They are typically interested in sentiment-oriented ratable aspects (i.e., semantic aspects). However, extracting semantic aspects across domains is challenging. We propose a domain-independent topic sentiment model called Joint Multi-grain Topic Sentiment (JMTS) to extract semantic aspects. JMTS effectively extracts quality semantic aspects automatically, thereby eliminating the requirement for manual probing. We conduct both qualitative and quantitative comparisons to evaluate JMTS. The experimental results confirm that JMTS generates semantic aspects with correlated top words and outperforms state-of-the-art models in several performance metrics. (C) 2016 Elsevier Inc. All rights reserved. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | ELSEVIER SCIENCE INC | - |
dc.title | Joint multi-grain topic sentiment: modeling semantic aspects for online reviews | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Lee, SangKeun | - |
dc.identifier.doi | 10.1016/j.ins.2016.01.013 | - |
dc.identifier.scopusid | 2-s2.0-84963820824 | - |
dc.identifier.wosid | 000370910800014 | - |
dc.identifier.bibliographicCitation | INFORMATION SCIENCES, v.339, pp.206 - 223 | - |
dc.relation.isPartOf | INFORMATION SCIENCES | - |
dc.citation.title | INFORMATION SCIENCES | - |
dc.citation.volume | 339 | - |
dc.citation.startPage | 206 | - |
dc.citation.endPage | 223 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.subject.keywordAuthor | Opinion mining | - |
dc.subject.keywordAuthor | Topic model | - |
dc.subject.keywordAuthor | Aspect discovery | - |
dc.subject.keywordAuthor | Sentiment analysis | - |
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