Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

7-2023

Abstract

The rapid development of aspect-based sentiment analysis (ABSA) within recent decades shows great potential for real-world society. The current ABSA works, however, are mostly limited to the scenario of a single text piece, leaving the study in dialogue contexts unexplored. To bridge the gap between fine-grained sentiment analysis and conversational opinion mining, in this work, we introduce a novel task of conversational aspect-based sentiment quadruple analysis, namely DiaASQ, aiming to detect the quadruple of target-aspect-opinion-sentiment in a dialogue. We manually construct a large-scale high-quality DiaASQ dataset in both Chinese and English languages. We deliberately develop a neural model to benchmark the task, which advances in effectively performing end-to-end quadruple prediction, and manages to incorporate rich dialogue-specific and discourse feature representations for better cross-utterance quadruple extraction. We hope the new benchmark will spur more advancements in the sentiment analysis community.

Keywords

Chinese language, English languages, Fine grained, High quality, Large-scales, Novel task, Opinion mining, Real-world, Sentiment analysis

Discipline

Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics, Toronto, Canada, 2023 July 9-14

First Page

13449

Last Page

13467

ISBN

9781959429623

Identifier

10.18653/v1/2023.findings-acl.849

Publisher

ACL

City or Country

Texas

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.18653/v1/2023.findings-acl.849

Download

Share

COinS