Leveraging Transformer-Based Models for Post-Market Surveillance: A Case Study on Turkish E-Commerce Reviews of Diabetes Management Devices

Leveraging Transformer-Based Models for Post-Market Surveillance: A Case Study on Turkish E-Commerce Reviews of Diabetes Management Devices | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 16 February 2026 V1 Latest version Share on Leveraging Transformer-Based Models for Post-Market Surveillance: A Case Study on Turkish E-Commerce Reviews of Diabetes Management Devices Authors : Emrah ATILGAN , Menderes Tarcan 0000-0003-3989-2440 [email protected] , Alperen Evci 0009-0003-8695-5528 , and Neset HIKMET Authors Info & Affiliations https://doi.org/10.22541/au.177123975.54573004/v1 130 views 55 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Background: Sentiment analysis in Turkish is particularly challenging due to the language’s agglutinative structure and rich morphological variation. At the same time, consumer-generated data on healthcare products—especially self-monitoring blood glucose (SMBG) systems—represent an underutilized source of real-world evidence for understanding device performance and user experience. Objective: This study aims to develop a Bidirectional Encoder Representations from Transformers (BERT) based sentiment analysis model for Turkish e-commerce reviews and to evaluate its applicability to SMBG devices as a potential complementary tool for post-market surveillance. This study utilizes an infoveillance approach to bridge the gap between traditional vigilance systems and real-world patient experiences, providing a scalable model for digital health surveillance in non-English speaking contexts. Furthermore, we demonstrate how this infoveillance framework can support health policy decision-making by providing real-time oversight of medical device safety. Methods: A pre-trained Turkish BERT model was fine-tuned to classify sentiments into positive, negative, and neutral categories. A corpus of 75,000 Turkish e-commerce reviews, one of the largest manually annotated datasets for Turkish sentiment analysis, was collected and processed through tokenization, cleaning, and manual labeling. Model performance was compared with traditional machine-learning baselines to assess the benefits of contextual embeddings. Results: The fine-tuned BERT model outperformed all baseline classifiers, achieving higher overall accuracy and class-wise F1-scores. In the SMBG subset, sentiment distribution was 89.44% positive, 7.22% negative, and 3.33% neutral, indicating that the model successfully captured fine-grained linguistic cues and provided robust insights into user perspectives. Conclusion: The findings demonstrate that BERT-based models can effectively address the linguistic complexity of Turkish and extract meaningful sentiment patterns from consumer reviews of SMBG devices. These insights show potential value for regulatory and vigilance contexts, suggesting that consumer-generated data could serve as a complementary early-signal source in post-market device surveillance. Future work may integrate multimodal inputs and explainability techniques to improve domain adaptability and transparency. Supplementary Material File (1-paper_r.docx) Download 4.04 MB Information & Authors Information Version history V1 Version 1 16 February 2026 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords natural language processing post-marketing surveillance self-monitoring blood glucose (smbg) devices transformer models turkish language Authors Affiliations Emrah ATILGAN Eskisehir Osmangazi Universitesi Muhendislik Mimarlik Fakultesi View all articles by this author Menderes Tarcan 0000-0003-3989-2440 [email protected] Eskisehir Osmangazi Universitesi View all articles by this author Alperen Evci 0009-0003-8695-5528 Eskisehir Osmangazi Universitesi Muhendislik Mimarlik Fakultesi View all articles by this author Neset HIKMET University of South Carolina View all articles by this author Metrics & Citations Metrics Article Usage 130 views 55 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Emrah ATILGAN, Menderes Tarcan, Alperen Evci, et al. Leveraging Transformer-Based Models for Post-Market Surveillance: A Case Study on Turkish E-Commerce Reviews of Diabetes Management Devices. Authorea . 16 February 2026. DOI: https://doi.org/10.22541/au.177123975.54573004/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. 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