Aspect-based sentiment analysis as fine-grained opinion mining
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 solel...
| Main Authors: | , , |
|---|---|
| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2020
|
| Subjects: | |
| Online Access: | https://eprints.nottingham.ac.uk/64081/ |
| _version_ | 1848800087456612352 |
|---|---|
| author | Diaz, Gerardo Ocampo Zhang, Xuanming Ng, Vincent |
| author_facet | Diaz, Gerardo Ocampo Zhang, Xuanming Ng, Vincent |
| author_sort | Diaz, Gerardo Ocampo |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T20:45:59Z |
| format | Conference or Workshop Item |
| id | nottingham-64081 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T20:45:59Z |
| publishDate | 2020 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-640812020-12-21T06:17:44Z https://eprints.nottingham.ac.uk/64081/ Aspect-based sentiment analysis as fine-grained opinion mining Diaz, Gerardo Ocampo Zhang, Xuanming Ng, Vincent 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. 2020-05-16 Conference or Workshop Item PeerReviewed application/pdf en cc_by https://eprints.nottingham.ac.uk/64081/1/Aspect-based%20sentiment%20analysis%20as%20fine-grained%20opinion%20mining.pdf 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. opinion mining; sentiment analysis; text mining |
| spellingShingle | opinion mining; sentiment analysis; text mining Diaz, Gerardo Ocampo Zhang, Xuanming Ng, Vincent Aspect-based sentiment analysis as fine-grained opinion mining |
| title | Aspect-based sentiment analysis as fine-grained opinion mining |
| title_full | Aspect-based sentiment analysis as fine-grained opinion mining |
| title_fullStr | Aspect-based sentiment analysis as fine-grained opinion mining |
| title_full_unstemmed | Aspect-based sentiment analysis as fine-grained opinion mining |
| title_short | Aspect-based sentiment analysis as fine-grained opinion mining |
| title_sort | aspect-based sentiment analysis as fine-grained opinion mining |
| topic | opinion mining; sentiment analysis; text mining |
| url | https://eprints.nottingham.ac.uk/64081/ |