Relational graph convolutional networks for sentiment analysis

Khosravi, Asal; Rahmati, Zahed; Vefghi, Ali

doi:10.22034/cmde.2025.65816.3048

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	Relational graph convolutional networks for sentiment analysis
Computational Methods for Differential Equations
مقاله 11، دوره 14، شماره 3، مهر 2026، صفحه 1165-1179 اصل مقاله (2 M)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22034/cmde.2025.65816.3048
نویسندگان
Asal Khosravi؛ Zahed Rahmati^* ؛ Ali Vefghi
Department of Mathematics and Computer Science, Amirkabir University of Technology, Tehran, Iran.
چکیده
With the growth of textual data across online platforms, sentiment analysis is essential for deriving insights from user-generated content. While traditional approaches and deep learning models have shown promise, they often cannot capture complex relationships between entities. In this paper, we propose using Relational Graph Convolutional Networks (RGCNs) for sentiment analysis, which provide better interpretability by modeling dependencies between data points represented as interconnected nodes in a graph structure. We demonstrate our method’s effectiveness through pretrained language models such as BERT and RoBERTa with RGCN architecture on product reviews from Amazon and Digikala datasets and analyze the resulting performance. Our experiments underscore the strength of RGCNs in capturing relational information for sentiment analysis tasks.
کلیدواژه‌ها
Heterogeneous graphs؛ Sentiment analysis؛ Graph neural networks؛ Relational graph convolutional networks؛ Pretrained language models

مراجع
[1] K. Church and P. Hanks, Word association norms, mutual information, and lexicography, Comput. Linguistics,16(1) (1990), 22–29. [2] G. Ciano, A. Rossi, M. Bianchini, and F. Scarselli, On inductive–transductive learning with graph neural networks, IEEE Trans. Pattern Anal. Mach. Intell., 44(2) (2021), 758–769. [3] Y. Dai, L. Shou, M. Gong, X. Xia, Z. Kang, Z. Xu, and D. Jiang, Graph fusion network for text classification, Knowledge-based Systems, 236 (2022), 107659. [4] J. Devlin, M. W. Chang, K. Lee, and K. Toutanova, Bert: Pre-training of deep bidirectional transformers for language understanding, arXiv preprint arXiv:1810.04805 (2018). [5] M. Farahani, M. Gharachorloo, M. Farahani, and M. Manthouri, Parsbert: Transformer-based model for persian language understanding, Neural Processing Lett., 53 (2021), 3831–3847. [6] P. Jaccard, Etude comparative de la distribution florale dans une portion des Alpes et des Jura, Bull Soc Vaudoise Sci Nat, 37 (1901), 547–579. [7] F. Lei, X. Liu, Z. Li, Q. Dai, and S. Wang, Multihop neighbor information fusion graph convolutional network for text classification, Mathematical Problems in Engineering, 2021 (2021), 1–9. [8] Y. Lin, Y. Meng, X. Sun, Q. Han, K. Kuang, J. Li, and F. Wu, Bertgcn: Transductive text classification by combining gcn and bert, arXiv preprint arXiv:2105.05727, (2021). [9] Y. Liu, M. Ott, N. Goyal, J. Du, M. Joshi, D. Chen, O. Levy, M. Lewis, L. Zettlemoyer, and V. Stoyanov, Roberta: A robustly optimized bert pretraining approach, arXiv preprint arXiv:1907.11692, (2019). [10] X. Liu, X. You, X. Zhang, J. Wu, and P. Lv, Tensor graph convolutional networks for text classification, In Proceedings of the AAAI conference on artificial intelligence, 34-05 (2020), 8409–8416. [11] A. Marin, R. Holenstein, R. Sarikaya, and M. Ostendorf, Learning phrase patterns for text classification using a knowledge graph and unlabeled data., in Interspeech (2014), 253–257. [12] M. Ostendorff, P. Bourgonje, M. Berger, J. Moreno-Schneider, G. Rehm, and B. Gipp, Enriching bert with knowledge graph embeddings for document classification, arXiv preprint arXiv:1909.08402, (2019). [13] R. Ragesh, S. Sellamanickam, A. Iyer, R. Bairi, and V. Lingam, Hetegcn: heterogeneous graph convolutional networks for text classification, in Proceedings of the 14th ACM international conference on web search and data mining, (2021), 860–868. [14] G. Salton, Introduction to modern information retrieval, McGraw-Hill, (1983). [15] M. Schlichtkrull, T. N. Kipf, P. Bloem, R. Van Den Berg, I. Titov, and M. Welling, Modeling relational data with graph convolutional networks, in The semantic web: 15th international conference, ESWC 2018, Heraklion, Crete, Greece, June 3–7, (2018), 593–607. [16] H. Tang, Y. Mi, F. Xue, and Y. Cao, An integration model based on graph convolutional network for text classification, IEEE Access, 8 (2020), 148865–148876. [17] K. Wang, S.C. Han, S. Long, and J. Poon, Me-gcn: Multi-dimensional edge-embedded graph convolutional networks for semi-supervised text classification, arXiv preprint arXiv:2204.04618, (2022). [18] X. Wang, D. Bo, C. Shi, S. Fan, Y. Ye, and P.S. Yu, A survey on heterogeneous graph embedding: methods, techniques, applications and sources, IEEE Trans. Big Data, 9(2) (2022), 415–436. [19] K. Wang, S.C. Han, and J. Poon, Induct-gcn: Inductive graph convolutional networks for text classification, in 2022 26th International Conference on Pattern Recognition (ICPR), (2022), 1243 [20] F. Wu, A. Souza, T. Zhang, C. Fifty, T. Yu, and K. Weinberger, Simplifying graph convolutional networks, in International conference on machine learning, (2019), 6861–6871. [21] Q. Xie, J. Huang, P. Du, M. Peng, and J. Y. Nie, Inductive topic variational graph auto-encoder for text classification, in proceedings of the 2021 conference of the North American chapter of the association for computational linguistics: human language technologies, (2021), 4218–4227. [22] L. Yao, C. Mao, and Y. Luo, Graph convolutional networks for text classification, in Proceedings of the AAAI conference on artificial intelligence, 33=01 (2019), 7370–7377. [23] H. Zhang and J. Zhang, Text graph transformer for document classification, in Conference on empirical methods in natural language processing (EMNLP), (2020). [24] H. Zhu and P. Koniusz, Simple spectral graph convolution, In international conference on learning representations, (2020).
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Relational graph convolutional networks for sentiment analysis