Author(s)

Can Jia, Azragu^*

Affiliation(s)

College of Computer Science and Technology, Xinjiang Normal University, Urumqi 830054, China.

Corresponding Author

Azragu

ABSTRACT

In order to solve the problems of insufficient coordination between global semantics and local syntactic features and noise interference of dependency parsing in aspect sentiment classification tasks, this paper proposes a bidirectional semantic enhancement and hierarchical syntactic analysis model based on graph convolutional network (GCN). The model effectively integrates semantic enhancement GCN and syntactic enhancement GCN for feature interaction to accurately model the complex hierarchical relationship between aspect words and sentiment words. In semantic modeling, self-attention and perceptual aspect attention (ASA) are integrated to extract deep semantic information through the attention fusion mechanism (GAFM). In terms of syntactic feature extraction, the syntactic distance mask matrix is introduced to measure semantic association, and the syntactic modeling is optimized in combination with the dependency relationship. In terms of structural optimization, the hierarchical phrase structure is adopted to fuse the syntactic dependency matrix with the phrase matrix to significantly reduce the noise of dependency tree parsing. Experimental results show that the model performs well on multiple datasets, with consistently improved accuracy and stability. Ablation experiments and visualization analysis further verify the effectiveness of each module, proving that the combination of bidirectional semantic enhancement and hierarchical syntactic analysis helps substantially improve the performance of aspect sentiment classification.

KEYWORDS

Graph convolutional network; Semantic enhancement; Syntactic enhancement; Feature interaction; Aspect sentiment classification

CITE THIS PAPER

Can Jia, Azragu. Bidirectional semantic and hierarchical syntactic sentiment classification based on GCN. Journal of Computer Science and Electrical Engineering. 2025, 7(2): 35-41. DOI: https://doi.org/10.61784/jcsee3045.

REFERENCES

[1] Zhou J, Huang J X, Hu Q V, et al. Sk-gcn: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowledge-Based Systems, 2020, 205: 106292.

[2] Gu Yuying, Gao Meifeng. Aspect-level sentiment analysis based on the fusion of part-of-speech and external knowledge . Computer Science and Exploration, 2022(06):1-13.

[3] Chen G, Tian Y, Song Y. Joint Aspect Extraction and Sentiment Analysis with Directional Graph Convolutional Networks//Proceedings of the 28th International Conference on Computational Linguistics. 2020: 272-279.

[4] Zhang Chen, Li Qiuchi, Song Dawei. Aspect-based sentiment classification with aspect-specific graph con-volutional networks / /Proceedings of the 2019 Con-ference on Empirical. Methods in Natural Language Pro- cessing and the 9th International Joint Conference on Natural Language Processing(EMNLP-IJCNLP), Hong Kong, China, November 3-7, 2019, New York: ACM, 2019: 4567-4577.

[5] Nguyen T H, Shirai K. Aspect-Based Sentiment Analysis Using Tree Kernel Based Relation Extraction//The 16th International Conference on Intelligent Text Processing and Computational Linguistics (CICLing), Cairo, Apr 14-20, 2015. Berlin: Springer, 2015: 114-125.

[6] Gu Yuying, Gao Meifeng. Aspect-level sentiment analysis based on the fusion of part-of-speech and external knowledge. Computer Science and Exploration, 2022(06): 1-13.

[7] Fan F, Feng Y, Zhao D. Multi-grained attention network for aspect-level sentiment classification//Proceedings of the 2018 conference on empirical methods in natural language processing. 2018: 3433-3442.

[8] Yang H, Zeng B, Yang J,et al. A multi-task learning model for Chinese-oriented aspect polarity classification and aspect term extraction.Neurocomputing, 2020, 419:344-356.

[9] Song Y, Wang J, Jiang T, et al. Attentional encoder network for targeted sentiment classification. arXiv preprint arXiv:1902.09314, 2019.

[10] Wang K, Shen W Z, Yang Y Y, et al. Relational Graph Attention Network for Aspect-based Sentiment Analysis//Pro-ceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020: 3229-3238.

[11] Zhang Z, Zhou Z, Wang Y. SSEGCN: Syntactic and semantic enhanced graph convolutional network for aspect-based sentiment analysis//Proceedings of the 2022 conference of the North American chapter of the association for computational Linguistics: human language technologies. 2022: 4916-4925.