TIIS (한국인터넷정보학회)
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| 한글제목(Korean Title) |
Attention Capsule Network for Aspect-Level Sentiment Classification |
| 영문제목(English Title) |
Attention Capsule Network for Aspect-Level Sentiment Classification |
| 저자(Author) |
Yu Deng
Hang Lei
Xiaoyu Li
Yiou Lin
Wangchi Cheng
Shan Yang
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| 원문수록처(Citation) |
VOL 15 NO. 4 PP. 1275 ~ 1292 (2021. 04) |
한글내용
(Korean Abstract) |
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영문내용
(English 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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| 키워드(Keyword) |
Capsule Network
Convolutional Neural Network
Aspect-level Sentiment Classification
Natural Language Processing
Attention Mechanism
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| 파일첨부 |
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