Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/139596
Full metadata record
DC FieldValueLanguage
dc.contributor.authorPeng, Haiyunen_US
dc.contributor.authorMa, Yukunen_US
dc.contributor.authorLi, Yangen_US
dc.contributor.authorCambria, Eriken_US
dc.date.accessioned2020-05-20T07:36:05Z-
dc.date.available2020-05-20T07:36:05Z-
dc.date.issued2018-
dc.identifier.citationPeng, H., Ma, Y., Li, Y., & Cambria, E. (2018). Learning multi-grained aspect target sequence for Chinese sentiment analysis. Knowledge-Based Systems, 148, 167-176. doi:10.1016/j.knosys.2018.02.034en_US
dc.identifier.issn0950-7051en_US
dc.identifier.urihttps://hdl.handle.net/10356/139596-
dc.description.abstractAspect-based sentiment analysis aims at identifying sentiment polarity towards aspect targets in a sentence. Previously, the task was modeled as a sentence-level sentiment classification problem that treated aspect targets as a hint. Such approaches oversimplify the problem by averaging word embeddings when the aspect target is a multi-word sequence. In this paper, we formalize the problem from a different perspective, i.e., that sentiment at aspect target level should be the main focus. Due to the fact that written Chinese is very rich and complex, Chinese aspect targets can be studied at three different levels of granularity: radical, character and word. Thus, we propose to explicitly model the aspect target and conduct sentiment classification directly at the aspect target level via three granularities. Moreover, we study two fusion methods for such granularities in the task of Chinese aspect-level sentiment analysis. Experimental results on a multi-word aspect target subset from SemEval2014 and four Chinese review datasets validate our claims and show the improved performance of our model over the state of the art.en_US
dc.language.isoenen_US
dc.relation.ispartofKnowledge-Based Systemsen_US
dc.rights© 2018 Elsevier B.V. All rights reserved.en_US
dc.subjectEngineering::Computer science and engineeringen_US
dc.titleLearning multi-grained aspect target sequence for Chinese sentiment analysisen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.identifier.doi10.1016/j.knosys.2018.02.034-
dc.identifier.scopus2-s2.0-85042932426-
dc.identifier.volume148en_US
dc.identifier.spage167en_US
dc.identifier.epage176en_US
dc.subject.keywordsAspect-based Sentiment Analysisen_US
dc.subject.keywordsChinese NLPen_US
item.grantfulltextnone-
item.fulltextNo Fulltext-
Appears in Collections:SCSE Journal Articles

SCOPUSTM   
Citations 5

69
Updated on Mar 10, 2021

PublonsTM
Citations 5

46
Updated on Mar 7, 2021

Page view(s)

111
Updated on Jan 25, 2022

Google ScholarTM

Check

Altmetric


Plumx

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.