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<p>Morphological complexity and dialectal variation make Arabic one of the most demanding languages for automated sentiment analysis (SA). To tackle these limitations, this work proposes a hybrid framework that couples a Large Language Model with a Graph Neural Network (LLM–GNN), combining the complementary strengths of contextual text encoding and relational structure learning. Specifically, AraBERT v2 serves as the backbone encoder, while a Graph Convolutional Network (GCN) built on cosine-similarity edges captures inter-sentence dependencies that purely sequential architectures tend to overlook. The framework is benchmarked on the publicly available Arabic 100k Reviews dataset (99,999 authentic user-generated reviews balanced equally across Positive, Negative, and Mixed sentiment classes). Against four established baselines (fine-tuned AraBERT, AraBERT-BiLSTM, AraBERT-MLP, multilingual BERT and modern Arabic-centric LLMs such as Jais-13B), the proposed model achieves an overall accuracy of 66.9% and a macro F1-score of 66.55%, representing gains of 7.6% and 4.4% over the strongest comparable baseline, respectively. Training curves indicate stable loss reduction from the earliest epochs, reflecting consistent optimization behavior throughout the 50-epoch schedule. A noted constraint is that graph construction operates at the mini-batch level, which limits the model’s exposure to corpus-wide semantic relationships. Nevertheless, the results confirm that integrating graph-based relational reasoning with transformer-derived embeddings surfaces nuanced sentiment signals that sequential models routinely miss, pointing to practical utility in Arabic social media monitoring and large-scale customer review analysis.</p>

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Integrating Large Language Models and Graph Neural Networks for enhanced Arabic sentiment analysis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月24日

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  • Hani Iwidat

摘要

Morphological complexity and dialectal variation make Arabic one of the most demanding languages for automated sentiment analysis (SA). To tackle these limitations, this work proposes a hybrid framework that couples a Large Language Model with a Graph Neural Network (LLM–GNN), combining the complementary strengths of contextual text encoding and relational structure learning. Specifically, AraBERT v2 serves as the backbone encoder, while a Graph Convolutional Network (GCN) built on cosine-similarity edges captures inter-sentence dependencies that purely sequential architectures tend to overlook. The framework is benchmarked on the publicly available Arabic 100k Reviews dataset (99,999 authentic user-generated reviews balanced equally across Positive, Negative, and Mixed sentiment classes). Against four established baselines (fine-tuned AraBERT, AraBERT-BiLSTM, AraBERT-MLP, multilingual BERT and modern Arabic-centric LLMs such as Jais-13B), the proposed model achieves an overall accuracy of 66.9% and a macro F1-score of 66.55%, representing gains of 7.6% and 4.4% over the strongest comparable baseline, respectively. Training curves indicate stable loss reduction from the earliest epochs, reflecting consistent optimization behavior throughout the 50-epoch schedule. A noted constraint is that graph construction operates at the mini-batch level, which limits the model’s exposure to corpus-wide semantic relationships. Nevertheless, the results confirm that integrating graph-based relational reasoning with transformer-derived embeddings surfaces nuanced sentiment signals that sequential models routinely miss, pointing to practical utility in Arabic social media monitoring and large-scale customer review analysis.