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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

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Showing 1-25 of 107 citing articles:

Dual Graph Convolutional Networks for Aspect-based Sentiment Analysis
Ruifan Li, Hao Chen, Fangxiang Feng, et al.
(2021)
Open Access | Times Cited: 255
A Survey on Aspect-Based Sentiment Analysis: Tasks, Methods, and Challenges
Wenxuan Zhang, Xin Li, Yang Deng, et al.
IEEE Transactions on Knowledge and Data Engineering (2022) Vol. 35, Iss. 11, pp. 11019-11038
Open Access | Times Cited: 243
Enhanced Multi-Channel Graph Convolutional Network for Aspect Sentiment Triplet Extraction
Hao Chen, Zepeng Zhai, Fangxiang Feng, et al.
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (2022)
Open Access | Times Cited: 86
SSEGCN: Syntactic and Semantic Enhanced Graph Convolutional Network for Aspect-based Sentiment Analysis
Zheng Zhang, Zili Zhou, Yanna Wang
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (2022)
Open Access | Times Cited: 81
Knowledge Graph Augmented Network Towards Multiview Representation Learning for Aspect-Based Sentiment Analysis
Qihuang Zhong, Liang Ding, Juhua Liu, et al.
IEEE Transactions on Knowledge and Data Engineering (2023) Vol. 35, Iss. 10, pp. 10098-10111
Open Access | Times Cited: 75
Discrete Opinion Tree Induction for Aspect-based Sentiment Analysis
Chenhua Chen, Zhiyang Teng, Zhongqing Wang, et al.
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (2022)
Open Access | Times Cited: 69
Incorporating Dynamic Semantics into Pre-Trained Language Model for Aspect-based Sentiment Analysis
Kai Zhang, Kun Zhang, Mengdi Zhang, et al.
Findings of the Association for Computational Linguistics: ACL 2022 (2022)
Open Access | Times Cited: 49
BiSyn-GAT+: Bi-Syntax Aware Graph Attention Network for Aspect-based Sentiment Analysis
Shuo Liang, Wei Wei, Xian-Ling Mao, et al.
Findings of the Association for Computational Linguistics: ACL 2022 (2022)
Open Access | Times Cited: 48
On the Robustness of Aspect-based Sentiment Analysis: Rethinking Model, Data, and Training
Hao Fei, Tat‐Seng Chua, Chenliang Li, et al.
ACM transactions on office information systems (2022) Vol. 41, Iss. 2, pp. 1-32
Open Access | Times Cited: 45
Enhancing aspect-based sentiment analysis using a dual-gated graph convolutional network via contextual affective knowledge
Hongtao Liu, Yiming Wu, Qingyu Li, et al.
Neurocomputing (2023) Vol. 553, pp. 126526-126526
Closed Access | Times Cited: 28
Unified Instance and Knowledge Alignment Pretraining for Aspect-Based Sentiment Analysis
Juhua Liu, Qihuang Zhong, Liang Ding, et al.
IEEE/ACM Transactions on Audio Speech and Language Processing (2023) Vol. 31, pp. 2629-2642
Open Access | Times Cited: 21
Incorporating syntax and semantics with dual graph neural networks for aspect-level sentiment analysis
Pengcheng Wang, Linping Tao, Mingwei Tang, et al.
Engineering Applications of Artificial Intelligence (2024) Vol. 133, pp. 108101-108101
Closed Access | Times Cited: 11
RDGCN: Reinforced Dependency Graph Convolutional Network for Aspect-based Sentiment Analysis
Xusheng Zhao, Hao Peng, Qiong Dai, et al.
(2024), pp. 976-984
Open Access | Times Cited: 11
Learn from structural scope: Improving aspect-level sentiment analysis with hybrid graph convolutional networks
Lvxiaowei Xu, Xiaoxuan Pang, Jianwang Wu, et al.
Neurocomputing (2022) Vol. 518, pp. 373-383
Open Access | Times Cited: 36
Graph Neural Networks for Natural Language Processing: A Survey
Lingfei Wu, Yu Chen, Kai Shen, et al.
(2023)
Open Access | Times Cited: 20
Fine-Grained Sentiment Analysis Based on SSFF-GCN Model
Yuexu Zhao, Junjie Fang, Shaolong Jin
Systems (2025) Vol. 13, Iss. 2, pp. 111-111
Open Access
Sentiment interaction and multi-graph perception with graph convolutional networks for aspect-based sentiment analysis
Qiang Lu, Xia Sun, Richard F. E. Sutcliffe, et al.
Knowledge-Based Systems (2022) Vol. 256, pp. 109840-109840
Closed Access | Times Cited: 26
Aspect-Based Sentiment Analysis with Explicit Sentiment Augmentations
Jihong Ouyang, Zhiyao Yang, Silong Liang, et al.
Proceedings of the AAAI Conference on Artificial Intelligence (2024) Vol. 38, Iss. 17, pp. 18842-18850
Open Access | Times Cited: 4
MulGCN: MultiGraph Convolutional Network for Aspect-level Sentiment Analysis
Huyen Trang Phan, Van Du Nguyen, Ngoc Thanh Nguyên
IEEE Access (2025) Vol. 13, pp. 26304-26317
Open Access
Syntax-based residual graph attention network for aspect-level sentiment classification
Guangtao Xu, Zhihao Yang, Jinzhong Ning, et al.
Knowledge-Based Systems (2025), pp. 113279-113279
Closed Access
Hierarchical-enhanced graph convolutional networks leveraging causal inference for aspect-based sentiment analysis
Fengling Zhou, Zhixin Li, Canlong Zhang, et al.
Applied Intelligence (2025) Vol. 55, Iss. 7
Closed Access
SSEMGAT: Syntactic and Semantic Enhanced Multi-Layer Graph Attention Network for Aspect-Level Sentiment Analysis
Xiangzhe Xin, Aishan Wumaier, Zaokere Kadeer, et al.
Applied Sciences (2023) Vol. 13, Iss. 8, pp. 5085-5085
Open Access | Times Cited: 12
AMR-based Network for Aspect-based Sentiment Analysis
Fukun Ma, Xuming Hu, Aiwei Liu, et al.
(2023)
Open Access | Times Cited: 11
Span-level Aspect-based Sentiment Analysis via Table Filling
Zhang Mao, Yongxin Zhu, Zhen Liu, et al.
(2023)
Open Access | Times Cited: 11
To be Closer: Learning to Link up Aspects with Opinions
Yuxiang Zhou, Lejian Liao, Yang Gao, et al.
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (2021), pp. 3899-3909
Open Access | Times Cited: 26
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