Aspect-based sentiment analysis as fine-grained opinion mining

Gerardo Ocampo Diaz, Xuanming Zhang, Vincent Ng

Research output: Contribution to conferencePaper

1 Citation (Scopus)
7 Downloads (Pure)

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.
Original languageEnglish
Pages6804-6811
Publication statusPublished - 16 May 2020
Event12th Language Resources and Evaluation Conference - Marseille, France
Duration: 11 May 202016 May 2020

Conference

Conference12th Language Resources and Evaluation Conference
Period11/05/2016/05/20

Keywords

  • opinion mining
  • sentiment analysis
  • text mining

Fingerprint

Dive into the research topics of 'Aspect-based sentiment analysis as fine-grained opinion mining'. Together they form a unique fingerprint.

Cite this