A multi-layer network for aspect-based cross-lingual sentiment classification
DC Field | Value | Language |
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dc.contributor.author | Sattar, Kalim | - |
dc.contributor.author | Umer, Qasim | - |
dc.contributor.author | Vasbieva, Dinara G. | - |
dc.contributor.author | Chung, Sungwook | - |
dc.contributor.author | Latif, Zohaib | - |
dc.contributor.author | Lee, Choonhwa | - |
dc.date.accessioned | 2022-07-06T12:12:46Z | - |
dc.date.available | 2022-07-06T12:12:46Z | - |
dc.date.created | 2021-12-08 | - |
dc.date.issued | 2021-09 | - |
dc.identifier.issn | 2169-3536 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/140966 | - |
dc.description.abstract | In the recent era, the advancement of communication technologies provides a valuable interaction source between people of different regions. Nowadays, many organizations adopt the latest approaches, i.e., sentiment analysis and aspect-oriented sentiment classification, to evaluate user reviews to improve the quality of their products. The processing of multi-lingual user reviews is a key challenge in Natural Language Processing (NLP). This paper proposes a multi-layer network with divided attention to perform aspect-based sentiment classification for cross-lingual data. It extracts the Part-of-Speech (POS) tagging information of the given reviews, preprocesses them, and converts them into tokens. Furthermore, bi-lingual dictionaries are leveraged to map the converted tokens from one language to another. Given the preprocessed and mapped reviews, vectors are generated by leveraging the multi-lingual BERT and passed to the proposed deep learning classifier. The 10351 restaurant reviews from SemEval-2016 Task 5 dataset are exploited for the prediction of aspect-based sentiment. The results of cross-lingual validation suggest that the proposed approach significantly outperforms the state-of-the-art approaches and improves the precision, recall, and F1 by more than 23%, 20%, and 22%, respectively. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.title | A multi-layer network for aspect-based cross-lingual sentiment classification | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Lee, Choonhwa | - |
dc.identifier.doi | 10.1109/ACCESS.2021.3116053 | - |
dc.identifier.scopusid | 2-s2.0-85116976446 | - |
dc.identifier.wosid | 000704096900001 | - |
dc.identifier.bibliographicCitation | IEEE ACCESS, v.9, pp.133961 - 133973 | - |
dc.relation.isPartOf | IEEE ACCESS | - |
dc.citation.title | IEEE ACCESS | - |
dc.citation.volume | 9 | - |
dc.citation.startPage | 133961 | - |
dc.citation.endPage | 133973 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | Y | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Telecommunications | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Telecommunications | - |
dc.subject.keywordAuthor | Sentiment analysis | - |
dc.subject.keywordAuthor | Task analysis | - |
dc.subject.keywordAuthor | Data models | - |
dc.subject.keywordAuthor | Feature extraction | - |
dc.subject.keywordAuthor | Data mining | - |
dc.subject.keywordAuthor | Bit error rate | - |
dc.subject.keywordAuthor | Tagging | - |
dc.subject.keywordAuthor | Natural language processing | - |
dc.subject.keywordAuthor | cross-lingual | - |
dc.subject.keywordAuthor | divided attention | - |
dc.subject.keywordAuthor | aspect-based sentiment classification | - |
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