HGTBF-OM: A Hybrid Graph-Based Transformer Framework for Enhanced Opinion Mining in Textual Data

As one such process, opinion mining carries immense significance in deriving actionable insights from textual information, facilitating data-driven decisions across sectors. While existing methodologies have attained great strides, including hybrid deep learning models and artificial neural networks, they need help addressing deep sentiments and contextual relationships in complex datasets. The current state-of-the-art approaches either is reliant on heuristics-based methods such as LSTMs or independent graph learning techniques or less sophisticated strategies based on either one of graph-based techniques or domain transformers without ensembling their respective characteristics, thus causing them to lose the power of harmony between the two as a pioneering approach. To solve these challenges, this study presents the Hybrid Graph Transformer-Based Framework for Opinion Mining (HGTBF-OM). It integrates graph representation, TransformerConv layers, and multi-head attention mechanisms. Experimental validation on the Zomato dataset demonstrated that the proposed framework outperformed current models, achieving a commendable accuracy of 99.01%, surpassing TransLSTM, So-haTRed, and Hybrid GCN-RF. The focus on these results demonstrates the framework’s facet-wise analysis capacity and its performance on balanced data. It is helpful for e-commerce, healthcare, and social media monitoring applications where the precision of sentiment analysis is vital. This framework addresses the limitations of classical methods and provides a scalable, efficient approach to contemporary opinion-related sentiment analysis tasks. Future work plans to broaden its use on multi-lingual datasets, improve computational efficiency, and assess its usability in dynamic and task-specific sentiment analysis scenarios.