Aspect Sentiment Classification with Aspect-Specific Opinion Spans

Lu Xu, Lidong Bing, Wei Lu, Fei Huang


Abstract
Aspect based sentiment analysis, predicting sentiment polarity of given aspects, has drawn extensive attention. Previous attention-based models emphasize using aspect semantics to help extract opinion features for classification. However, these works are either not able to capture opinion spans as a whole, or not able to capture variable-length opinion spans. In this paper, we present a neat and effective structured attention model by aggregating multiple linear-chain CRFs. Such a design allows the model to extract aspect-specific opinion spans and then evaluate sentiment polarity by exploiting the extracted opinion features. The experimental results on four datasets demonstrate the effectiveness of the proposed model, and our analysis demonstrates that our model can capture aspect-specific opinion spans.
Anthology ID:
2020.emnlp-main.288
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3561–3567
Language:
URL:
https://aclanthology.org/2020.emnlp-main.288
DOI:
10.18653/v1/2020.emnlp-main.288
Bibkey:
Cite (ACL):
Lu Xu, Lidong Bing, Wei Lu, and Fei Huang. 2020. Aspect Sentiment Classification with Aspect-Specific Opinion Spans. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 3561–3567, Online. Association for Computational Linguistics.
Cite (Informal):
Aspect Sentiment Classification with Aspect-Specific Opinion Spans (Xu et al., EMNLP 2020)
Copy Citation:
PDF:
https://preview.aclanthology.org/update-css-js/2020.emnlp-main.288.pdf
Video:
 https://slideslive.com/38939301
Code
 xuuuluuu/Aspect-Sentiment-Classification
Data
SemEval 2014 Task 4 Sub Task 2