Exploiting Subjectivity Knowledge Transfer for End-to-End Aspect-Based Sentiment Analysis
| Autor: | Samuel Pecar, Marián Šimko |
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| Rok vydání: | 2021 |
| Předmět: | |
| Zdroj: | Text, Speech, and Dialogue ISBN: 9783030835262 TDS |
| DOI: | 10.1007/978-3-030-83527-9_23 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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