Target-Aspect-Sentiment Joint Detection for Aspect-Based Sentiment Analysis

Hai Wan, Yufei Yang, Jianfeng Du, Yanan Liu, Kunxun Qi, Jeff Z. Pan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

Aspect-based sentiment analysis (ABSA) aims to detect the targets (which are composed by continuous words), aspects and sentiment polarities in text. Published datasets from SemEval-2015 and SemEval-2016 reveal that a sentiment polarity depends on both the target and the aspect. However, most of the existing methods consider predicting sentiment polarities from either targets or aspects but not from both, thus they easily make wrong predictions on sentiment polarities. In particular, where the target is implicit, i.e., it does not appear in the given text, the methods predicting sentiment polarities from targets do not work. To tackle these limitations in ABSA, this paper proposes a novel method for target-aspect-sentiment joint detection. It relies on a pre-trained language model and can capture the dependence on both targets and aspects for sentiment prediction. Experimental results on the SemEval-2015 and SemEval-2016 restaurant datasets show that the proposed method achieves a high performance in detecting target-aspect-sentiment triples even for the implicit target cases; moreover, it even outperforms the state-of-the-art methods for those subtasks of target-aspect-sentiment detection that they are competent to.
Original languageEnglish
Title of host publicationProceedings of the AAAI Conference on Artificial Intelligence
Place of PublicationPalo Alto, California, USA
PublisherAssociation for the Advancement of Artificial Intelligence AAAI
Number of pages8
ISBN (Print)978-1-57735-835-0
Publication statusPublished - 3 Apr 2020

Publication series

NameProceedings of the AAAI Conference on Artificial Intelligence
ISSN (Print)2159-5399
ISSN (Electronic)2374-3468


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