• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

Attention Capsule Network for Aspect-Level Sentiment Classification


Abstract

As a fine-grained classification problem, aspect-level sentiment classification predicts the sentiment polarity for different aspects in context. To address this issue, researchers have widely used attention mechanisms to abstract the relationship between context and aspects. Still, it is difficult to effectively obtain a more profound semantic representation, and the strong correlation between local context features and the aspect-based sentiment is rarely considered. In this paper, a hybrid attention capsule network for aspect-level sentiment classification (ABASCap) was proposed. In this model, the multi-head self-attention was improved, and a context mask mechanism based on adjustable context window was proposed, so as to effectively obtain the internal association between aspects and context. Moreover, the dynamic routing algorithm and activation function in capsule network were optimized to meet the task requirements. Finally, sufficient experiments were conducted on three benchmark datasets in different domains. Compared with other baseline models, ABASCap achieved better classification results, and outperformed the state-of-the-art methods in this task after incorporating pre-training BERT.


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Cite this article

[IEEE Style]
Y. Deng, H. Lei, X. Li, Y. Lin, W. Cheng and S. Yang, "Attention Capsule Network for Aspect-Level Sentiment Classification," KSII Transactions on Internet and Information Systems, vol. 15, no. 4, pp. 1275-1292, 2021. DOI: 10.3837/tiis.2021.04.006.

[ACM Style]
Yu Deng, Hang Lei, Xiaoyu Li, Yiou Lin, Wangchi Cheng, and Shan Yang. 2021. Attention Capsule Network for Aspect-Level Sentiment Classification. KSII Transactions on Internet and Information Systems, 15, 4, (2021), 1275-1292. DOI: 10.3837/tiis.2021.04.006.

[BibTeX Style]
@article{tiis:24524, title="Attention Capsule Network for Aspect-Level Sentiment Classification", author="Yu Deng and Hang Lei and Xiaoyu Li and Yiou Lin and Wangchi Cheng and Shan Yang and ", journal="KSII Transactions on Internet and Information Systems", DOI={10.3837/tiis.2021.04.006}, volume={15}, number={4}, year="2021", month={April}, pages={1275-1292}}