Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

11-2021

Abstract

Aspect-based sentiment analysis (ABSA) typically focuses on extracting aspects and predicting their sentiments on individual sentences such as customer reviews. Recently, another kind of opinion sharing platform, namely question answering (QA) forum, has received increasing popularity, which accumulates a large number of user opinions towards various aspects. This motivates us to investigate the task of ABSA on QA forums (ABSA-QA), aiming to jointly detect the discussed aspects and their sentiment polarities for a given QA pair. Unlike review sentences, a QA pair is composed of two parallel sentences, which requires interaction modeling to align the aspect mentioned in the question and the associated opinion clues in the answer. To this end, we propose a model with a specific design of cross-sentence aspect-opinion interaction modeling to address this task. The proposed method is evaluated on three real-world datasets and the results show that our model outperforms several strong baselines adopted from related state-of-the-art models.

Discipline

Databases and Information Systems

Research Areas

Data Science and Engineering

Areas of Excellence

Digital transformation

Publication

Proceedings of the 2021 Findings of the Association for Computational Linguistics, Virtual Conference, November 7-11

First Page

4582

Last Page

4591

Identifier

10.18653/v1/2021.findings-emnlp.390

Publisher

Association for Computational Linguistics

City or Country

USA

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.18653/v1/2021.findings-emnlp.390

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