The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
Interactive Attention Networks for Aspect-Level Sentiment Classification
Open accessAbstract
Aspect-level sentiment classification aims at identifying the sentiment polarity of specific target in its context. Previous approaches have realized the importance of targets in sentiment classification and developed various methods with the goal of precisely modeling thier contexts via generating target-specific representations. However, these studies always ignore the separate modeling of targets. In this paper, we argue that both targets and contexts deserve special treatment and need to be learned their own representations via interactive learning. Then, we propose the interactive attention networks (IAN) to interactively learn attentions in the contexts and targets, and generate the representations for targets and contexts separately. With this design, the IAN model can well represent a target and its collocative context, which is helpful to sentiment classification. Experimental results on SemEval 2014 Datasets demonstrate the effectiveness of our model.
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Cites
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Attention-based LSTM for Aspect-level Sentiment Classification
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A General Survey on Attention Mechanisms in Deep Learning
Brauwers, G., & Frasincar, F. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3126456 · Cited by 695
An Attentive Survey of Attention Models
Chaudhari, S., Mithal, V., Polatkan, G., & Ramanath, R. (2021) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3465055 · Cited by 689
Relational Graph Attention Network for Aspect-based Sentiment Analysis
Wang, K., Shen, W., Yang, Y., Quan, X., & Wang, R. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.295 · Cited by 665
Deep Learning for Aspect-Based Sentiment Analysis: A Comparative Review
Do, H. H., Prasad, P., Maag, A., & Alsadoon, A. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.10.003 · Cited by 614
A Survey on Aspect-Based Sentiment Analysis: Tasks, Methods, and Challenges
Zhang, W., Li, X., Deng, Y., Bing, L., & Lam, W. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2022.3230975 · Cited by 567
Metadata
- Authors
- Ma, D., Li, S., Zhang, X., & Wang, H.
- Venue
- Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
- Published
- 2017-08
- Pages
- 4068-4074
- OpenAlex
- W2964164368
- Cited by
- 1,069
- References
- 24
- FWCI
- 44.33
- Type
- conference paper
- Language
- en
- Open access
- bronze
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