Aspect-Based Sentiment Analysis as Fine-Grained Opinion Mining

Gerardo Ocampo Diaz, Xuanming Zhang and Vincent Ng.
Proceedings of the 12th International Conference on Language Resources and Evaluation, 2020.

Click here for the PDF version.

Abstract

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.

BibTeX entry

@InProceedings{Ocampo+etal:20a,
  author = {Mojica, Luis Gerardo and Xuanming Zhang and Vincent Ng},
  title = {Aspect-Based Sentiment Analysis as Fine-Grained Opinion Mining},
  booktitle = {Proceedings of the 12th International Conference on Language Resources and Evaluation},
  year = 2020}