EHSAN: Leveraging ChatGPT in a Hybrid Framework for Arabic Aspect-Based Sentiment Analysis in Healthcare

Arabic - language patient feedback remains under - analysed because dialect diversity and scarce aspect - level sentiment labels hinder automated assessment. To address this gap, we introduce EHSAN, a data - centric hybrid pipeline that merges ChatGPT pseudo - label ling with targeted human review to build the first explainable Arabic aspect - based sentiment dataset for healthcare. Each sentence is annotated with an aspect and sentiment label (positive, negative, or neutral), forming a pioneering Arabic dataset aligned with healthcare themes, with ChatGPT - generated rationales provided for each label to enhance transparency. To evaluate the impact of annotation quality on model performance, we created three versions of the training data: a fully supervised set with all l abels reviewed by humans, a semi - supervised set with 50% human review, and an unsupervised set with only machine - generated labels. We fine - tuned two transformer models on these datasets for both aspect and sentiment classification. Experimental results sho w that our Arabic - specific model achieved high accuracy even with minimal human supervision, reflecting only a minor performance drop when using ChatGPT - only labels. Reducing the number of aspect classes notably improved classification metrics across the b oard. These findings demonstrate an effective, scalable approach to Arabic aspect - based sentiment analysis (SA) in healthcare, combining large language model annotation with human expertise to produce a robust and explainable dataset. Future directions inc lude generalisation across hospitals, prompt refinement, and interpretable data - driven modelling.