Abstract
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-labelling 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 labels 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 show 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 board. 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 include generalisation across hospitals, prompt refinement, and interpretable data-driven modelling.
Abstract (translated)
阿拉伯语患者反馈的分析仍然不足，主要是因为方言多样性以及情感标签稀缺阻碍了自动化评估。为了填补这一空白，我们引入了一个名为EHSAN的数据中心化混合管道，该系统结合了ChatGPT伪标注和定向人工审核，以构建首个解释性的阿拉伯语基于方面的医疗保健情感数据集。每个句子都用一个方面及其相应的情感标签（正面、负面或中立）进行了注释，形成了与健康主题对齐的开创性阿拉伯语数据集，并且为每一个标签提供了ChatGPT生成的理由，以此来增强透明度。 为了评估标注质量对模型性能的影响，我们创建了训练数据的三个版本：完全监督的数据集（所有标签都由人类审核）、半监督数据集（50%的人工审核）和无监督数据集（仅包含机器生成的标签）。我们在这些数据集上微调了两个转换器模型以进行基于方面的情感分类。实验结果显示，即使在最小人工监督的情况下，我们的阿拉伯语特定模型也达到了高准确率，而使用ChatGPT生成的标签时，其性能仅略有下降。 值得注意的是，在将情感类别减少到更少数量后，所有类别的分类指标都有显著改善。这些发现表明了一种有效的、可扩展的方法来在医疗保健领域进行基于方面的阿拉伯语情感分析（SA），通过结合大型语言模型标注和人类专业知识来生成稳健且解释性高的数据集。未来的方向包括医院间的推广、提示优化以及基于数据的可解释建模方法的发展。
URL
https://arxiv.org/abs/2508.02574
