A hybrid large language model-graph neural network framework for Arabic sentiment analysis

Abstract

Arabic sentiment analysis (SA) faces significant challenges due to the language’s morphological richness and dialectal diversity. This study introduces a novel hybrid large language model-graph neural network (LLM-GNN) framework designed to address these challenges. The proposed model integrates the contextual understanding of AraBERT v2 with the structural learning capability of a graph convolutional network (GCN). It constructs a graph of sentences using cosine similarity, allowing the GCN to capture crucial inter-sentence semantic dependencies often missed by sequential models. Findings: The model is evaluated on a publicly available Arabic 100k Reviews dataset consisting of authentic user-generated Arabic reviews balanced across positive, negative, and mixed sentiment classes. The results demonstrate that the proposed LLM-GNN model performed better as compared to the baseline models, including fine-tuned AraBERT, AraBERT-BiLSTM, AraBERT-MLP, and multilingual BERT. The hybrid model achieves an overall accuracy of 66.8% and a F1-score of 66.55%, with an improvement of 7.6% and 4.4%, respectively. The model demonstrated stable convergence from the first training epoch. Research limitations/implications: The graph construction is performed at the mini batch level, which restricts the modeling of global semantic relationships across the entire corpus. The results show that the hybrid model identifies subtle sentiment cues that sequential models frequently miss by fusing relational graph reasoning with contextual embeddings. By effectively identifying subtle sentiment cues, the hybrid model can significantly enhance the accuracy of real-world applications such as social media monitoring and customer review analysis for Arabic content.