Improving Implicit Sentiments Analysis via Explanations of Multiple Perspectives
Implicit sentiment expressions convey emotions indirectly, through context or factual statements, rather than explicit opinion words. Recent research on implicit sentiment analysis overlooks the fact that various individuals can interpret the same implicit expressions in different manners and experi...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10947047/ |
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| author | An Wang Huidong Jiang Youmi Ma Junfeng Jiang Ao Liu Naoaki Okazaki |
| author_facet | An Wang Huidong Jiang Youmi Ma Junfeng Jiang Ao Liu Naoaki Okazaki |
| author_sort | An Wang |
| collection | DOAJ |
| description | Implicit sentiment expressions convey emotions indirectly, through context or factual statements, rather than explicit opinion words. Recent research on implicit sentiment analysis overlooks the fact that various individuals can interpret the same implicit expressions in different manners and experience different sentiments. Additionally, most previous research mainly focuses on implicit sentiment classification, neglecting the reasons behind the results. It hinders the deep understanding of the complexities involved in human emotions and limits the application of sentiment analysis. In this work, we introduce a new task, Abisa-Ex, which aims at both sentiment classification and explanation generation. We re-labeled the previous aspect-based implicit sentiment analysis dataset, incorporating new (sentiment, explanation) pair labels provided by various annotators. Based on the new dataset, we design frameworks to allow models to learn to predict sentiments from different perspectives and provide reasonable explanations jointly. Notably, our work shows that learning explanations from various viewpoints not only allows the model to generate the logical process behind sentiment analysis, but also significantly improves the model’s sentiment classification performance. |
| format | Article |
| id | doaj-art-e6055ceaed7f4d4d8ca3f6854e78c392 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-e6055ceaed7f4d4d8ca3f6854e78c3922025-08-20T02:16:33ZengIEEEIEEE Access2169-35362025-01-0113611366114810.1109/ACCESS.2025.355676210947047Improving Implicit Sentiments Analysis via Explanations of Multiple PerspectivesAn Wang0https://orcid.org/0000-0001-8503-643XHuidong Jiang1https://orcid.org/0009-0003-3859-0536Youmi Ma2https://orcid.org/0000-0002-8576-7563Junfeng Jiang3Ao Liu4https://orcid.org/0000-0001-6785-6282Naoaki Okazaki5Department of Computer Science, Institute of Science Tokyo, Tokyo, JapanDepartment of Computer Science, Institute of Science Tokyo, Tokyo, JapanDepartment of Computer Science, Institute of Science Tokyo, Tokyo, JapanDepartment of Computer Science, The University of Tokyo, Tokyo, JapanTencent Inc., Shenzhen, ChinaDepartment of Computer Science, Institute of Science Tokyo, Tokyo, JapanImplicit sentiment expressions convey emotions indirectly, through context or factual statements, rather than explicit opinion words. Recent research on implicit sentiment analysis overlooks the fact that various individuals can interpret the same implicit expressions in different manners and experience different sentiments. Additionally, most previous research mainly focuses on implicit sentiment classification, neglecting the reasons behind the results. It hinders the deep understanding of the complexities involved in human emotions and limits the application of sentiment analysis. In this work, we introduce a new task, Abisa-Ex, which aims at both sentiment classification and explanation generation. We re-labeled the previous aspect-based implicit sentiment analysis dataset, incorporating new (sentiment, explanation) pair labels provided by various annotators. Based on the new dataset, we design frameworks to allow models to learn to predict sentiments from different perspectives and provide reasonable explanations jointly. Notably, our work shows that learning explanations from various viewpoints not only allows the model to generate the logical process behind sentiment analysis, but also significantly improves the model’s sentiment classification performance.https://ieeexplore.ieee.org/document/10947047/Sentiment analysisimplicit sentimentexplanation generation |
| spellingShingle | An Wang Huidong Jiang Youmi Ma Junfeng Jiang Ao Liu Naoaki Okazaki Improving Implicit Sentiments Analysis via Explanations of Multiple Perspectives IEEE Access Sentiment analysis implicit sentiment explanation generation |
| title | Improving Implicit Sentiments Analysis via Explanations of Multiple Perspectives |
| title_full | Improving Implicit Sentiments Analysis via Explanations of Multiple Perspectives |
| title_fullStr | Improving Implicit Sentiments Analysis via Explanations of Multiple Perspectives |
| title_full_unstemmed | Improving Implicit Sentiments Analysis via Explanations of Multiple Perspectives |
| title_short | Improving Implicit Sentiments Analysis via Explanations of Multiple Perspectives |
| title_sort | improving implicit sentiments analysis via explanations of multiple perspectives |
| topic | Sentiment analysis implicit sentiment explanation generation |
| url | https://ieeexplore.ieee.org/document/10947047/ |
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