{"id":9620,"date":"2021-09-23T14:59:55","date_gmt":"2021-09-23T12:59:55","guid":{"rendered":"https:\/\/kinit.sk\/publication\/exploiting-subjectivity-knowledge-transfer-for-end-to-end-aspect-based-sentiment-analysis\/"},"modified":"2026-04-23T14:32:05","modified_gmt":"2026-04-23T12:32:05","slug":"exploiting-subjectivity-knowledge-transfer-for-end-to-end-aspect-based-sentiment-analysis","status":"publish","type":"publication","link":"https:\/\/kinit.sk\/sk\/publikacia\/exploiting-subjectivity-knowledge-transfer-for-end-to-end-aspect-based-sentiment-analysis\/","title":{"rendered":"Exploiting Subjectivity Knowledge Transfer for End-to-End Aspect-Based Sentiment Analysis"},"content":{"rendered":"
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Pecar, S.1<\/sup>, Simko, M. <\/strong><\/p>\n<\/div>\n\n

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1<\/sup> Faculty of Electrical Engineering and Informatics, Technical University of Kosice, Kosice, Slovakia<\/em><\/p>\n<\/div>\n\n

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While classic aspect-based sentiment analysis typically includes three sub-tasks (aspect extraction, opinion extraction, and aspect-level sentiment classification), recent studies focus on exploring possibilities of knowledge sharing from different tasks, such as document-level sentiment analysis or document-level domain classification that are less demanding on dataset resources. Several recent studies managed to propose different frameworks for solving nearly complete end-to-end aspect-based sentiment analysis in a unified manner. However, none of them studied the possibility of transferring knowledge about their subjectivity or opinion typology between sub-tasks. In this work, we propose subjectivity-aware learning as a novel auxiliary task for aspect-based sentiment analysis. Besides, we also propose another novel task defined as opinion type detection. We performed extensive experiments on the state-of-the-art dataset that show improvement of model performance while employing subjectivity learning. All models report improvement in overall F1 score for aspect-based sentiment analysis. In addition, we also set new benchmark results for the separate task of subjectivity detection and opinion type detection for the restaurant domain of SemEval 2015 dataset.<\/p>\n<\/div>\n\n

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Cite: Pecar, S., Simko, M. Exploiting Subjectivity Knowledge Transfer for End-to-End Aspect-Based Sentiment Analysis. International Conference on Text, Speech, and Dialogue (2021). 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