The DOI matches its Crossref record; 34 of the 34 references it cites were found in a registry or an index.
Relational Graph Attention Network for Aspect-based Sentiment Analysis
Open accessAbstract
Aspect-based sentiment analysis aims to determine the sentiment polarity towards a specific aspect in online reviews.Most recent efforts adopt attention-based neural network models to implicitly connect aspects with opinion words.However, due to the complexity of language and the existence of multiple aspects in a single sentence, these models often confuse the connections.In this paper, we address this problem by means of effective encoding of syntax information.Firstly, we define a unified aspect-oriented dependency tree structure rooted at a target aspect by reshaping and pruning an ordinary dependency parse tree.Then, we propose a relational graph attention network (R-GAT) to encode the new tree structure for sentiment prediction.Extensive experiments are conducted on the SemEval 2014 and Twitter datasets, and the experimental results confirm that the connections between aspects and opinion words can be better established with our approach, and the performance of the graph attention network (GAT) is significantly improved as a consequence.
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Cites
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Attention-based LSTM for Aspect-level Sentiment Classification
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SemEval-2014 Task 4: Aspect Based Sentiment Analysis
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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
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
Target-level sentiment analysis for news articles
Žitnik, S., Blagus, N., & Bajec, M. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.108939 · Cited by 32
HONGAT: Graph Attention Networks in the Presence of High-Order Neighbors
Zhang, H.-K., Zhang, Y.-G., Zhou, Z., & Li, Y.-F. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i15.29615 · Cited by 14
Metadata
- Authors
- Wang, K., Shen, W., Yang, Y., Quan, X., & Wang, R.
- Venue
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
- Published
- 2020
- Pages
- 3229-3238
- OpenAlex
- W3035529900
- Cited by
- 665
- References
- 34
- FWCI
- 35.78
- Type
- conference paper
- Language
- en
- Open access
- hybrid
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