Aspect-based sentiment analysis as fine-grained opinion mining

Diaz, Gerardo Ocampo, Zhang, Xuanming and Ng, Vincent (2020) Aspect-based sentiment analysis as fine-grained opinion mining. In: 12th Language Resources and Evaluation Conference, May 11-16, 2020, Marseille, France.

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We show how the general fine-grained opinion mining concepts of opinion target and opinion expression are related to aspect-based sentiment analysis (ABSA) and discuss their benefits for resource creation over popular ABSA annotation schemes. Specifically, we first discuss why opinions modeled solely in terms of (entity, aspect) pairs inadequately captures the meaning of the sentiment originally expressed by authors and how opinion expressions and opinion targets can be used to avoid the loss of information. We then design a meaning-preserving annotation scheme and apply it to two popular ABSA datasets, the 2016 SemEval ABSA Restaurant and Laptop datasets. Finally, we discuss the importance of opinion expressions and opinion targets for next-generation ABSA systems. We make our datasets publicly available for download.

Item Type: Conference or Workshop Item (Paper)
Keywords: opinion mining; sentiment analysis; text mining
Schools/Departments: University of Nottingham Ningbo China > Faculty of Science and Engineering > School of Computer Science
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Depositing User: Wu, Cocoa
Date Deposited: 21 Dec 2020 06:17
Last Modified: 21 Dec 2020 06:17

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