{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T10:52:36Z","timestamp":1786099956756,"version":"3.56.0"},"reference-count":86,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2024,11,21]],"date-time":"2024-11-21T00:00:00Z","timestamp":1732147200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Saudi Data and AI Authority (SDAIA) and King Fahd University of Petroleum and Minerals (KFUPM)","award":["JRCAI-RG-08"],"award-info":[{"award-number":["JRCAI-RG-08"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>Online medical forums have emerged as vital platforms for patients to share their experiences and seek advice, providing a valuable, cost-effective source of feedback for medical service management. This feedback not only measures patient satisfaction and improves health service quality but also offers crucial insights into the effectiveness of medical treatments, pain management strategies, and alternative therapies. This study systematically identifies and categorizes key aspects of patient experiences, emphasizing both positive and negative sentiments expressed in their narratives. We collected a dataset of approximately 15,000 entries from various sections of the widely used medical forum, patient.info. Our innovative approach integrates content analysis with aspect-based sentiment analysis, deep learning techniques, and a large language model (LLM) to analyze these data. Our methodology is designed to uncover a wide range of aspect types reflected in patient feedback. The analysis revealed seven distinct aspect types prevalent in the feedback, demonstrating that deep learning models can effectively predict these aspect types and their corresponding sentiment values. Notably, the LLM with few-shot learning outperformed other models. Our findings enhance the understanding of patient experiences in online forums and underscore the utility of advanced analytical techniques in extracting meaningful insights from unstructured patient feedback, offering valuable implications for healthcare providers and medical service management.<\/jats:p>","DOI":"10.3390\/bdcc8120167","type":"journal-article","created":{"date-parts":[[2024,11,21]],"date-time":"2024-11-21T06:11:54Z","timestamp":1732169514000},"page":"167","update-policy":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Aspect-Based Sentiment Analysis of Patient Feedback Using Large Language Models"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0001-8088-590X","authenticated-orcid":false,"given":"Omer S.","family":"Alkhnbashi","sequence":"first","affiliation":[{"name":"Information and Computer Science Department, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia"},{"name":"Center for Applied and Translational Genomics (CATG), Mohammed Bin Rashid University of Medicine and Health Sciences (MBRU), Dubai Healthcare City, Dubai P.O. Box 505055, United Arab Emirates"},{"name":"College of Medicine, Mohammed Bin Rashid University of Medicine and Health Sciences (MBRU), Dubai Healthcare City, Dubai P.O. Box 505055, United Arab Emirates"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0002-0800-428X","authenticated-orcid":false,"given":"Rasheed","family":"Mohammad","sequence":"additional","affiliation":[{"name":"Department of Computer Sciences, College of Computing and Digital Technology, Birmingham City University, Birmingham B4 7XG, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0003-1058-0996","authenticated-orcid":false,"given":"Mohammad","family":"Hammoudeh","sequence":"additional","affiliation":[{"name":"Information and Computer Science Department, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"114015","DOI":"10.1016\/j.jbusres.2023.114015","article-title":"Classification of reviews of e-healthcare services to improve patient satisfaction: Insights from an emerging economy","volume":"164","author":"Dhajate","year":"2023","journal-title":"J. Bus. Res."},{"key":"ref_2","first-page":"1","article-title":"The language of patient feedback: A corpus linguistic study of online health communication","volume":"56","author":"Baker","year":"2019","journal-title":"Engl. Specif. Purp."},{"key":"ref_3","first-page":"1","article-title":"A mixed methods systematic review of the effects of patient online self-diagnosing in the \u2018smart-phone society\u2019 on the healthcare professional-patient relationship and medical authority","volume":"20","author":"Farnood","year":"2020","journal-title":"BMC Med. Inf. Decis. Mak."},{"key":"ref_4","unstructured":"NHS (2017). Involving People in Their Own Health and Care: Statutory Guidance for Clinical Commissioning Groups and NHS England, NHS."},{"key":"ref_5","unstructured":"NHS (2023, May 04). GP Patient Survey 2015\u20132016. Available online: https:\/\/2.zoppoz.workers.dev:443\/https\/www.england.nhs.uk\/statistics\/2016\/07\/07\/gp-patient-survey-2015-16\/."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"e047508","DOI":"10.1136\/bmjopen-2020-047508","article-title":"Qualitative study: Patients\u2019 enduring concerns about discussing internet use in general practice consultations","volume":"11","author":"Cuteanu","year":"2021","journal-title":"BMJ Open"},{"key":"ref_7","unstructured":"Hedges, L., and Couey, C. (2024, March 12). How Patients Use Online Reviews. Available online: https:\/\/2.zoppoz.workers.dev:443\/https\/www.softwareadvice.com\/resources\/how-patients-use-online-reviews\/."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3310\/hsdr07380","article-title":"Using online patient feedback to improve NHS services: The INQUIRE multimethod study","volume":"7","author":"Powell","year":"2019","journal-title":"Health Serv. Deliv. Res."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1177\/1355819619844540","article-title":"Online patient feedback: A cross-sectional survey of the attitudes and experiences of United Kingdom health care professionals","volume":"24","author":"Atherton","year":"2019","journal-title":"J. Health Serv. Res. Policy"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"e031820","DOI":"10.1136\/bmjopen-2019-031820","article-title":"Online patient feedback as a measure of quality in primary care: A multimethod study using correlation and qualitative analysis","volume":"10","author":"Boylano","year":"2020","journal-title":"BMJ Open"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2055207617728186","DOI":"10.1177\/2055207617728186","article-title":"VIEWPOINT: What counts as online patient feedback, and for whom?","volume":"3","author":"Dudhwala","year":"2017","journal-title":"Digit. Health"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1258\/jhsrp.2011.011029","article-title":"Which experiences of health care delivery matter to service users and why? A critical interpretive synthesis and conceptual map","volume":"17","author":"Entwistle","year":"2012","journal-title":"J. Health Serv. Res. Policy"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"e12454","DOI":"10.5812\/ircmj.12454","article-title":"Patient Involvement in Health Care Decision Making: A Review","volume":"16","author":"Vahdat","year":"2014","journal-title":"Iran. Red Crescent Med. J."},{"key":"ref_14","unstructured":"NIHR (2024, April 02). Improving Care by Using Patient Feedback. Available online: https:\/\/2.zoppoz.workers.dev:443\/https\/content.nihr.ac.uk\/nihrdc\/themedreview-04327-PE\/Patient-Feedback-WEB.pdf."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"102625","DOI":"10.1016\/j.technovation.2022.102625","article-title":"TripAdvisor of healthcare:Opportunities for value creation through patient feedback platforms","volume":"121","author":"Bez","year":"2023","journal-title":"Technovation"},{"key":"ref_16","first-page":"19","article-title":"Learning from patients\u2019 written feedback: Medical students\u2019 experiences","volume":"31","author":"Stenfors","year":"2022","journal-title":"Int. J. Med. Educ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"129","DOI":"10.5334\/pme.842","article-title":"Patients as Feedback Providers: Exploring Medical Students\u2019 Credibility Judgments","volume":"12","author":"Eijkelboom","year":"2023","journal-title":"Perspect. Med. Educ."},{"key":"ref_18","unstructured":"Fox, S. (2011). The Social Life of Health Information, Pew Research Center."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Jia, X., Pang, Y., and Liu, L. (2021). Sally Online Health Information Seeking Behavior: A Systematic Review. Healthcare, 9.","DOI":"10.3390\/healthcare9121740"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1177\/0972063413489058","article-title":"Use of social media marketing in healthcare","volume":"15","author":"Gupta","year":"2013","journal-title":"J. Health Manag."},{"key":"ref_21","first-page":"2042533313478004","article-title":"How do online patient support communities affect the experience of inflammatory bowel disease? An online survey","volume":"4","author":"Coulson","year":"2013","journal-title":"JRSM"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"e14321","DOI":"10.2196\/14321","article-title":"Web-Based Peer Support Interventions for Adults Living with Chronic Conditions: Scoping Review","volume":"8","author":"Hossain","year":"2021","journal-title":"JMIR Rehabil. Assist. Technol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/j.pec.2010.05.029","article-title":"An investigation into the empowerment effects of using online support groups and how this affects health professional\/patient communication","volume":"83","author":"Bartlett","year":"2011","journal-title":"Patient Educ. Couns."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Ziebland, S., Powell, J., Briggs, P., Jenkinson, C., Wyke, S., Sillence, E., Harris, P., Perera, R., Mazanderani, F., and Martin, A. (2016). Examining the Role of Patients\u2019 Experiences as a Resource for Choice and Decision-Making in Health Care: A Creative, Interdisciplinary Mixed-Method Study in Digital Health, NIHR Journals Library.","DOI":"10.3310\/pgfar04170"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"A197","DOI":"10.1016\/j.jval.2014.03.1151","article-title":"PRM95\u2014An open research exchange for online patient feedback in pro development","volume":"17","author":"Harrington","year":"2014","journal-title":"Value Health"},{"key":"ref_26","first-page":"138","article-title":"Physician Perceptions of Performance Feedback and Impact on Personal Well-Being: A Qualitative Exploration of Patient Satisfaction Feedback in Neurology","volume":"49","author":"Vilendrer","year":"2023","journal-title":"Jt. Comm. J. Qual. Patient Saf."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Sch\u00e4fer, H., Idrissi-Yaghir, A., Bewersdorff, J., Frihat, S., Friedrich, C.M., and Zesch, T. (2023). Medication event extraction in clinical notes: Contribution of the WisPerMed team to the n2c2 2022 challenge. J. Biomed. Inform., 143.","DOI":"10.1016\/j.jbi.2023.104400"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"McMaster, C., Chan, J., Liew, D.F., Su, E., Frauman, A.G., Chapman, W.W., and Pires, D.E. (2023). Developing a deep learning natural language processing algorithm for automated reporting of adverse drug reactions. J. Biomed. Inform., 137.","DOI":"10.1016\/j.jbi.2022.104265"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"114280","DOI":"10.1016\/j.socscimed.2021.114280","article-title":"Caring for care: Online feedback in the context of public healthcare services","volume":"285","author":"Mazanderani","year":"2021","journal-title":"Soc. Sci. Med."},{"key":"ref_30","unstructured":"Liu, W., Tang, J., Qin, J., Xu, L., Li, Z., and Liang, X. (2020). MedDG: A Large-scale Medical Consultation Dataset for Building Medical Dialogue System. arXiv."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Zeng, G., Yang, W., Ju, Y., Wang, S., Zhang, R., Zhou, M., Zeng, J., Dong, X., Zhang, R., and Fang, H. (2020, January 16\u201320). Meddialog: Large-scale medical dialogue datasets. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), Online.","DOI":"10.18653\/v1\/2020.emnlp-main.743"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Zhang, N., Chen, M., Bi, Z., Liang, X., Li, L., Shang, X., Yin, K., Tan, C., Xu, J., and Huang, F. (2022, January 22\u201327). Cblue: A Chinese biomedical language understanding evaluation benchmark. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Dublin, Ireland.","DOI":"10.18653\/v1\/2022.acl-long.544"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"941","DOI":"10.1007\/s10489-022-03400-y","article-title":"Constructing novel datasets for intent detection and Ner in a Korean healthcare advice system: Guidelines and empirical results","volume":"53","author":"Kim","year":"2022","journal-title":"Appl. Intell."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3458754","article-title":"Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing","volume":"3","author":"Gu","year":"2020","journal-title":"ACM Trans. Comput. Healthc."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Jin, Q., Dhingra, B., Liu, Z., Cohen, W.W., and Lu, X. (2019, January 7). Pubmedqa: A dataset for biomedical research question answering. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, EMNLP-IJCNLP, Hong Kong, China.","DOI":"10.18653\/v1\/D19-1259"},{"key":"ref_36","unstructured":"Huy, T.D., Tu, N.A., Vu, T.H., Minh, N.P., Phan, N., Bui, T.H., and Truong, S.Q. (2023). ViMQ: A Vietnamese Medical Question Dataset for Healthcare Dialogue System Development. Neural Information Processing, Springer."},{"key":"ref_37","unstructured":"Mondal, I., Ahuja, K., Jain, M., O\u2019Neill, J., Bali, K., and Choudhury, M. (2022, January 12\u201317). Global Readiness of Language Technology for Healthcare: What Would It Take to Combat the Next Pandemic?. Proceedings of the 29th International Conference on Computational Linguistics, Gyeongju, Republic of Korea."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Bai, G., He, S., Liu, K., and Zhao, J. (2022, January 22\u201327). Incremental intent detection for medical domain with contrast replay networks. Proceedings of the Findings of the Association for Computational Linguistics: ACL 2022, Dublin, Ireland.","DOI":"10.18653\/v1\/2022.findings-acl.280"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Bao, Q., Ni, L., and Liu, J. (2020, January 4\u20136). Hhh: An online medical chatbot system based on knowledge graph and hierarchical bi-directional attention. Proceedings of the Australasian Computer Science Week Multiconference, Melbourne, VIC, Australia.","DOI":"10.1145\/3373017.3373049"},{"key":"ref_40","unstructured":"Chen, Q., Zhuo, Z., and Wang, W. (2019). BERT for Joint Intent Classification and Slot Filling. arXiv."},{"key":"ref_41","unstructured":"Mehta, D., Santy, S., Mothilal, R.K., Srivastava, B.M.L., Sharma, A., Shukla, A., Prasad, V., Sharma, A., and Bali, K. (2020, January 11\u201316). Learnings from technological interventions in a low resource language: A case-study on Gondi. Proceedings of the Twelfth Language Resources and Evaluation Conference, Marseille, France."},{"key":"ref_42","unstructured":"Daniel, J.E., Brink, W., Eloff, R., and Copley, C. (August, January 28). Towards automating healthcare question answering in a noisy multilingual low-resource setting. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, Florence, Italy."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Mullick, A., Mondal, I., Ray, S., Raghav, R., Chaitanya, G.S., and Goyal, P. (2023). Intent Identification and Entity Extraction for Healthcare Queries in Indic Languages. arXiv.","DOI":"10.18653\/v1\/2023.findings-eacl.140"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Kotei, E., and Thirunavukarasu, R. (2023). A Systematic Review of Transformer-Based Pre-Trained Language Models through Self-Supervised Learning. Information, 14.","DOI":"10.3390\/info14030187"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12913-016-1691-0","article-title":"David Social media use in healthcare: A systematic review of effects on patients and on their relationship with healthcare professionals","volume":"16","author":"Smailhodzic","year":"2016","journal-title":"BMC Health Serv. Res."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1177\/1440783305050965","article-title":"The eMale: Prostate cancer, masculinity and online support as a challenge to medical expertise","volume":"41","author":"Broom","year":"2005","journal-title":"J. Sociol."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1572","DOI":"10.1177\/1359105312465915","article-title":"Communication online with fellow cancer patients: Writing to be remembered, gain strength, and find survivors","volume":"18","author":"Chiu","year":"2013","journal-title":"J. Health Psychol."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1111\/j.1369-7625.2009.00543.x","article-title":"Patient perspectives on health advice posted on Internet discussion boards: A qualitative study","volume":"12","author":"Armstrong","year":"2009","journal-title":"Health Expect."},{"key":"ref_49","first-page":"181","article-title":"The invisible reality of arthritis: A qualitative analysis of an online message board","volume":"6","author":"Hadert","year":"2008","journal-title":"Invis. Real. Arthritis: A Qual. Anal. Online Message Board"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1177\/1742395308097862","article-title":"Social interactions in an online self-management program for rheumatoid arthritis","volume":"4","author":"Shigaki","year":"2008","journal-title":"Chronic Illn."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1177\/17423953060020020601","article-title":"The personal impact of rheumatoid arthritis on patients\u2019 identity: A qualitative study","volume":"2","author":"Lempp","year":"2006","journal-title":"Chronic Illn."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"103513","DOI":"10.1016\/j.ipm.2023.103513","article-title":"Construction of an aspect-level sentiment analysis model for online medical reviews","volume":"60","author":"Zhao","year":"2023","journal-title":"Inf. Process. Manag."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"6633213","DOI":"10.1155\/2021\/6633213","article-title":"ABioNER: A BERT-based model for Arabic biomedical named-entity recognition","volume":"2021","author":"Boudjellal","year":"2021","journal-title":"Complexity"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Kalyan, K.S., Rajasekharan, A., and Sangeetha, S. (2022). AMMU: A survey of transformer-based biomedical pretrained language models. J. Biomed. Inform., 126.","DOI":"10.1016\/j.jbi.2021.103982"},{"key":"ref_55","unstructured":"Nerella, S., Bandyopadhyay, S., Zhang, J., Contreras, M., Siegel, S., Bumin, A., Silva, B., Sena, J., Shickel, B., and Bihorac, A. (2023). Transformers in Healthcare: A Survey. arXiv."},{"key":"ref_56","first-page":"1","article-title":"Pre-trained Language Models in Biomedical Domain: A Systematic Survey","volume":"56","author":"Wang","year":"2023","journal-title":"ACM Comput. Surv."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Nguyen, D.Q., Vu, T., and Nguyen, A.T. (2020, January 16\u201320). Bertweet: A pre-trained language model for English tweets. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, Online.","DOI":"10.18653\/v1\/2020.emnlp-demos.2"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"M\u00fcller, M., Salath\u00e9, M., Per, E., and Kummervold, P.E. (2023). COVID-Twitter-BERT: A natural language processing model to analyse COVID-19 content on Twitter. Front. Artif. Intell., 6.","DOI":"10.3389\/frai.2023.1023281"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1093\/bioinformatics\/btaa675","article-title":"The russian drug reaction corpus and neural models for drug reactions and effectiveness detection in user reviews","volume":"37","author":"Tutubalina","year":"2021","journal-title":"Bioinformatics"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"683","DOI":"10.1016\/j.procs.2024.06.077","article-title":"Analysing the patient sentiments in healthcare domain using Machine learning","volume":"238","author":"Madan","year":"2024","journal-title":"Procedia Comput. Sci."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Basaldella, M., Liu, F., Shareghi, E., and Collier, N. (2020, January 16\u201320). Cometa: A corpus for medical entity linking in the social media. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), Online.","DOI":"10.18653\/v1\/2020.emnlp-main.253"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Naseem, U., Khushi, M., Reddy, V., Rajendran, S., Razzak, I., and Kim, J. (2021, January 18\u201322). Bioalbert: A simple and effective pre-trained language model for biomedical named entity recognition. Proceedings of the 2021 International Joint Conference on Neural Networks (IJCNN), Shenzhen, China.","DOI":"10.1109\/IJCNN52387.2021.9533884"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1038\/s43856-023-00370-1","article-title":"The future landscape of large language models in medicine","volume":"3","author":"Clusmann","year":"2023","journal-title":"Commun. Med."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1038\/s41746-023-00958-w","article-title":"A study of generative large language model for medical research and healthcare","volume":"6","author":"Peng","year":"2023","journal-title":"NPJ Digit. Med."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"1930","DOI":"10.1038\/s41591-023-02448-8","article-title":"Large language models in medicine","volume":"29","author":"Thirunavukarasu","year":"2023","journal-title":"Nat. Med."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1038\/s41746-023-00879-8","article-title":"The shaky foundations of large language models and foundation models for electronic health records","volume":"6","author":"Wornow","year":"2023","journal-title":"NPJ Digit. Med."},{"key":"ref_67","unstructured":"Patient.Info (2023, December 15). About-Us. Available online: https:\/\/2.zoppoz.workers.dev:443\/https\/patient.info\/about-us."},{"key":"ref_68","first-page":"620","article-title":"Can I help you? Information sharing in online discussion forums by people living with a long-term condition","volume":"23","author":"Bond","year":"2016","journal-title":"J. Innov. Health Inf."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"1277","DOI":"10.1177\/1049732305276687","article-title":"Three Approaches to Qualitative Content Analysis","volume":"15","author":"Hsieh","year":"2005","journal-title":"Qual. Health Res."},{"key":"ref_70","unstructured":"Glenn (2024, May 20). Analyzing Open-Ended Questions. Available online: https:\/\/2.zoppoz.workers.dev:443\/http\/intelligentmeasurement.net\/2007\/12\/18\/analyzing-open-ended-questions\/."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"975","DOI":"10.1007\/s10209-022-00909-4","article-title":"Evaluating the performance of websites from a public value, usability, and readability perspectives: A review of Turkish national government websites","volume":"23","year":"2024","journal-title":"Univers. Access Inf. Soc."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1037\/h0057532","article-title":"A new readability yardstick","volume":"23","author":"Flesch","year":"1948","journal-title":"J. Appl. Psychol."},{"key":"ref_73","unstructured":"Zellers, R., Holtzman, A., Rashkin, H., Bisk, Y., Farhadi, A., Roesner, F., and Choi, Y. (2019). Defending against neural fake news. Adv. Neural Inf. Process. Syst., 32."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Fogg, B.J. (2003). Persuasive Technology: Using Computers to Change What We Think and Do, Morgan Kaufmann.","DOI":"10.1145\/764008.763957"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"103260","DOI":"10.1016\/j.ipm.2022.103260","article-title":"Back to common sense: Oxford dictionary descriptive knowledge augmentation for aspect-based sentiment analysis","volume":"60","author":"Jin","year":"2023","journal-title":"Inf. Process. Manag."},{"key":"ref_76","unstructured":"Yang, H., Zeng, B., Xu, M., and Wang, T. (2021). Back to Reality: Leveraging Pattern-driven Modeling to Enable Affordable Sentiment Dependency Learning. arXiv."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"101887","DOI":"10.1016\/j.inffus.2023.101887","article-title":"Enhancing social network hate detection using back translation and GPT-3 augmentations during training and test-time","volume":"99","author":"Cohen","year":"2023","journal-title":"Inf. Fusion"},{"key":"ref_78","unstructured":"Rolczy\u0144ski, R. (2021). Do You Trust in Aspect-Based Sentiment Analysis? Testing and Explaining Model Behaviors. Rafa\u0142 Rolczy\u0144ski, SCALAC SP. Z O. O."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.eng.2022.04.024","article-title":"Pre-Trained Language Models and Their Applications","volume":"25","author":"Wang","year":"2022","journal-title":"Engineering"},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Kam\u0131\u015f, S., and Goularas, D. (2019, January 26\u201328). Evaluation of Deep Learning Techniques in Sentiment Analysis from Twitter Data. Proceedings of the 2019 International Conference on Deep Learning and Machine Learning in Emerging Applications (Deep-ML), Istanbul, Turkey.","DOI":"10.1109\/Deep-ML.2019.00011"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1038\/s41586-023-06291-2","article-title":"Large language models encode clinical knowledge","volume":"620","author":"Singhal","year":"2023","journal-title":"Nature"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"103917","DOI":"10.1016\/j.ipm.2024.103917","article-title":"QAIE: LLM-based Quantity Augmentation and Information Enhancement for few-shot Aspect-Based Sentiment Analysis","volume":"62","author":"Lu","year":"2025","journal-title":"Inf. Process. Manag."},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Zhang, H., Zhang, Y., Zhan, L.M., Chen, J., Shi, G., Lam, A., and Wu, X.M. (2021). Effectiveness of Pre-Training for Few-Shot Intent Classification. arXiv.","DOI":"10.18653\/v1\/2021.findings-emnlp.96"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"20552076231155681","DOI":"10.1177\/20552076231155681","article-title":"The quality of informational social support in online health communities: A content analysis of cancer-related discussions","volume":"9","author":"Cugmas","year":"2023","journal-title":"Digit. Health"},{"key":"ref_85","first-page":"215","article-title":"Use of social media in health care by patients and health care professionals: Motives & barriers in Thailand","volume":"24","author":"Srimarut","year":"2019","journal-title":"Utop\u00eda Y Prax. Latinoam."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"1584","DOI":"10.1007\/s11096-021-01287-2","article-title":"Exploring patients\u2019 pharmacy stories: An analysis of online feedback","volume":"43","author":"Loo","year":"2021","journal-title":"Int. J. Clin. Pharm."}],"container-title":["Big Data and Cognitive Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/www.mdpi.com\/2504-2289\/8\/12\/167\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:36:39Z","timestamp":1760114199000},"score":1,"resource":{"primary":{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/www.mdpi.com\/2504-2289\/8\/12\/167"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,21]]},"references-count":86,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["bdcc8120167"],"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.3390\/bdcc8120167","relation":{},"ISSN":["2504-2289"],"issn-type":[{"value":"2504-2289","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,21]]}}}