{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T14:03:44Z","timestamp":1773842624122,"version":"3.50.1"},"reference-count":55,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2024,11,14]],"date-time":"2024-11-14T00:00:00Z","timestamp":1731542400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>Patient satisfaction is a key measure of the quality of healthcare, directly impacting the success and competitiveness of healthcare providers in an increasingly demanding market. Traditional feedback collection methods often fall short of capturing the full spectrum of patient experiences, leading to skewed satisfaction reports due to patients\u2019 reluctance to criticize services and the inherent limitations of survey designs. To address these issues, advanced Natural Language Processing (NLP) techniques such as aspect-based sentiment analysis are emerging as essential tools. Aspect-based sentiment analysis breaks down the feedback text into specific aspects and evaluates the sentiment for each aspect, offering a more nuanced and actionable understanding of patient opinions. Despite its potential, aspect-based sentiment analysis is under-explored in the healthcare sector, particularly in the Arabic literature. This study addresses this gap by performing an Arabic aspect-based sentiment analysis on patient experience data, introducing the newly constructed Hospital Experiences Arabic Reviews (HEAR) dataset, and conducting a comparative study using Bidirectional Embedding Representations from Transformers (BERT) combined with machine learning classifiers, as well as fine-tuning BERT models, including MARBERT, ArabicBERT, AraBERT, QARiB, and CAMeLBERT. Additionally, the performance of GPT-4 via OpenAI\u2019s ChatGPT is evaluated in this context, making a significant contribution to the comparative study of BERT with traditional classifiers and the assessment of GPT-4 for aspect-based sentiment analysis in healthcare, ultimately offering valuable insights for enhancing patient experiences through the use of AI-driven approaches. The results show that the joint model leveraging MARBERT and SVM achieves the highest accuracy of 92.14%, surpassing other models, including GPT-4, in both aspect category detection and polarity tasks.<\/jats:p>","DOI":"10.3390\/bdcc8110156","type":"journal-article","created":{"date-parts":[[2024,11,14]],"date-time":"2024-11-14T04:15:49Z","timestamp":1731557749000},"page":"156","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Listening to Patients: Advanced Arabic Aspect-Based Sentiment Analysis Using Transformer Models Towards Better Healthcare"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-3480-7942","authenticated-orcid":false,"given":"Seba","family":"AlNasser","sequence":"first","affiliation":[{"name":"Department of Computer Science, College of Computer and Information Sciences, King Saud University, P.O. Box 266, Riyadh 11362, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2642-072X","authenticated-orcid":false,"given":"Sarab","family":"AlMuhaideb","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Computer and Information Sciences, King Saud University, P.O. Box 266, Riyadh 11362, Saudi Arabia"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,14]]},"reference":[{"key":"ref_1","unstructured":"Smelser, N.J., and Baltes, P.B. (2001). Doctor\u2013Patient Interaction: Psychosocial Aspects. International Encyclopedia of the Social & Behavioral Sciences, Pergamon."},{"key":"ref_2","first-page":"195","article-title":"Analyzing Arabic Twitter-Based Patient Experience Sentiments Using Multi-Dialect Arabic Bidirectional Encoder Representations from Transformers","volume":"76","author":"AlMuhaideb","year":"2023","journal-title":"Comput. Mater. 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