AGCL: Aspect Graph Construction and Learning for Aspect-level Sentiment Classification
Zhongquan Jian, Daihang Wu, Shaopan Wang, Yancheng Wang, Junfeng Yao, Meihong Wang, Qingqiang Wu
Abstract
Prior studies on Aspect-level Sentiment Classification (ALSC) emphasize modeling interrelationships among aspects and contexts but overlook the crucial role of aspects themselves as essential domain knowledge. To this end, we propose AGCL, a novel Aspect Graph Construction and Learning method, aimed at furnishing the model with finely tuned aspect information to bolster its task-understanding ability. AGCL’s pivotal innovations reside in Aspect Graph Construction (AGC) and Aspect Graph Learning (AGL), where AGC harnesses intrinsic aspect connections to construct the domain aspect graph, and then AGL iteratively updates the introduced aspect graph to enhance its domain expertise, making it more suitable for the ALSC task. Hence, this domain aspect graph can serve as a bridge connecting unseen aspects with seen aspects, thereby enhancing the model’s generalization capability. Experiment results on three widely used datasets demonstrate the significance of aspect information for ALSC and highlight AGL’s superiority in aspect learning, surpassing state-of-the-art baselines greatly. Code is available at https://github.com/jian-projects/agcl.
BibTeX
@inproceedings{jian-etal-2025-agcl,
title = "{AGCL}: Aspect Graph Construction and Learning for Aspect-level Sentiment Classification",
author = "Jian, Zhongquan and
Wu, Daihang and
Wang, Shaopan and
Wang, Yancheng and
Yao, Junfeng and
Wang, Meihong and
Wu, Qingqiang",
editor = "Rambow, Owen and
Wanner, Leo and
Apidianaki, Marianna and
Al-Khalifa, Hend and
Eugenio, Barbara Di and
Schockaert, Steven",
booktitle = "Proceedings of the 31st International Conference on Computational Linguistics",
month = jan,
year = "2025",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.coling-main.56/",
pages = "841--854"
}