University of Tabriz Computational Methods for Differential Equations 2345-3982 14 3 2026 07 01 Relational graph convolutional networks for sentiment analysis 1165 1179 19870 10.22034/cmde.2025.65816.3048 EN Asal Khosravi Department of Mathematics and Computer Science, Amirkabir University of Technology, Tehran, Iran. Zahed Rahmati Department of Mathematics and Computer Science, Amirkabir University of Technology, Tehran, Iran. Ali Vefghi Department of Mathematics and Computer Science, Amirkabir University of Technology, Tehran, Iran. Journal Article 2025 02 08 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. https://cmde.tabrizu.ac.ir/article_19870_7fd3d9c86ef36506a2338bb12f8de215.pdf