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Aspect-level sentiment analysis based on semantic heterogeneous graph convolutional network

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Abstract

The deep learning methods based on syntactic dependency tree have achieved great success on Aspect-based Sentiment Analysis (ABSA). However, the accuracy of the dependency parser cannot be determined, which may keep aspect words away from its related opinion words in a dependency tree. Moreover, few models incorporate external affective knowledge for ABSA. Based on this, we propose a novel architecture to tackle the above two limitations, while fills up the gap in applying heterogeneous graphs convolution network to ABSA. Specially, we employ affective knowledge as an sentiment node to augment the representation of words. Then, linking sentiment node which have different attributes with word node through a specific edge to form a heterogeneous graph based on dependency tree. Finally, we design a multi-level semantic heterogeneous graph convolution network (Semantic-HGCN) to encode the heterogeneous graph for sentiment prediction. Extensive experiments are conducted on the datasets SemEval 2014 Task 4, SemEval 2015 task 12, SemEval 2016 task 5 and ACL 14 Twitter. The experimental results show that our method achieves the state-of-the-art performance.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant Nos. 62276073, 61966004), Guangxi Natural Science Foundation (No. 2019GXNSFDA245018), Innovation Project of Guangxi Graduate Education (No. YCSW2022155), Guangxi “Bagui Scholar” Teams for Innovation and Research Project, and Guangxi Collaborative Innovation Center of Multi-source Information Integration and Intelligent Processing.

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Authors and Affiliations

  1. Guangxi Key Lab of Multi-source Information Mining and Security, Guangxi Normal University, Guilin, 541004, China

    Yufei Zeng, Zhixin Li & Zhenbin Chen

  2. College of Computer Science and Engineering, Northwest Normal University, Lanzhou, 730070, China

    Huifang Ma

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  1. Yufei Zeng
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Corresponding author

Correspondence to Zhixin Li.

Additional information

Yufei Zeng is a master student at School of Computer Science and Engineering, Guangxi Normal University, China. His research interests include sentiment analysis and information extraction.

Zhixin Li is a professor at School of Computer Science and Engineering, Guangxi Normal University, China. In 2010, He obtained his PhD degree in computer software and theory from Institute of Computing Technology, Chinese Academy of Sciences, China. He obtained his BS degree and MS degree at the Huazhong University of Science and Technology, China in 1992 and 2004 respectively. His research interests include image understanding, machine learning and cross-media computing. He has won the best doctoral dissertation award of Chinese Association of Artificial Intelligence in 2011.

Zhenbin Chen is a master student at School of Computer Science and Engineering, Guangxi Normal University, China. His research interests include relation extraction and few-shot learning.

Huifang Ma received the BE degree from Northwest Normal University, China in 2003, and the MS degree from Beijing Normal University, China in 2006. She received the PhD degree from Institute of Computing Technology, Chinese Academy of Sciences, China in 2010. She is now a professor at College of Computer Science and Engineering, Northwest Normal University, China. Her research interests include data mining and machine learning.

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Zeng, Y., Li, Z., Chen, Z. et al. Aspect-level sentiment analysis based on semantic heterogeneous graph convolutional network. Front. Comput. Sci. 17, 176340 (2023). https://doi.org/10.1007/s11704-022-2256-5

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