Graph-Enhanced Transformer for Cross-Domain Sentiment Analysis: Integrating RoBERTa with Graph Attention Networks

Sentiment analysis has become a critical task in natural language processing for extracting subjective insights from large-scale textual data across domains such as social media, e-commerce, and online reviews. However, existing methods often fail to simultaneously capture contextual semantics and structural relationships, particularly in cross-domain settings. This study proposes a hybrid RoBERTa–graph attention network (GAT) framework that integrates transformer-based contextual embeddings with graph-based relational learning. The methodology involves encoding text using RoBERTa, constructing token-level dependency graphs, and applying multi-head graph attention to model inter-token relationships. The model is evaluated on multiple benchmark datasets, including Twitter, Amazon, and IMDB reviews. The cross-domain results refer only to binary-harmonized positive/negative sentiment transfer and should not be interpreted as full three-class sentiment transfer, including the neutral class. The experimental results show that the proposed approach achieves consistent improvements over the selected baseline models in terms of accuracy, F1-score, MCC, and AUC. Statistical robustness analysis further supports the stability of these improvements across repeated runs. The findings highlight the effectiveness of combining semantic and structural learning, making the proposed framework suitable for robust cross-domain sentiment analysis applications.