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Lookup NU author(s): Eman Alamoudi, Dr Ellis SolaimanORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
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.
Author(s): Alamoudi E, Solaiman E
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: Database Engineered Applications. IDEAS 2025
Year of Conference: 2025
Pages: 17–33
Print publication date: 01/11/2025
Online publication date: 01/11/2025
Acceptance date: 23/06/2025
Date deposited: 15/08/2025
ISSN: 0302-9743
Publisher: Springer
URL: https://doi.org/10.1007/978-3-032-06744-9_2
DOI: 10.1007/978-3-032-06744-9_2
ePrints DOI: 10.57711/nxhp-2162
Data Access Statement: An anonymised version of the EHSAN dataset and the experimental code has been archived on Zenodo for perpetual access (https://doi.org/10.5281/zenodo.15418860).
Library holdings: Search Newcastle University Library for this item
Series Title: Lecture Notes in Computer Science
ISBN: 9783032067432
