{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T15:33:51Z","timestamp":1777736031893,"version":"3.51.4"},"reference-count":32,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2025,6,13]],"date-time":"2025-06-13T00:00:00Z","timestamp":1749772800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Project P6.1\u2014Centralized Data Management Using BI (Business Intelligence) and AI (Artificial Intelligence)"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Applied Sciences"],"abstract":"<jats:p>The digital transformation of public services has led to a surge in the volume and complexity of informatics-related complaints, often marked by ambiguous language, inconsistent terminology, and fragmented reporting. Conventional keyword-based approaches are inadequate for detecting semantically similar issues expressed in diverse ways. This study proposes an AI-powered framework that employs BERT-based sentence embeddings, semantic clustering, and classification algorithms, structured under the CRISP-DM methodology, to standardize and automate complaint analysis. Leveraging real-world interaction logs from a public sector agency, the system harmonizes heterogeneous complaint narratives, uncovers latent issue patterns, and enables early detection of technical and usability problems. The approach is deployed through a real-time dashboard, transforming complaint handling from a reactive to a proactive process. Experimental results show a 27% reduction in repeated complaint categories and a 32% increase in classification efficiency. The study also addresses ethical concerns, including data governance, bias mitigation, and model transparency. This work advances citizen-centric service delivery by demonstrating the scalable application of AI in public sector informatics.<\/jats:p>","DOI":"10.3390\/app15126673","type":"journal-article","created":{"date-parts":[[2025,6,16]],"date-time":"2025-06-16T10:47:22Z","timestamp":1750070842000},"page":"6673","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Proactive Complaint Management in Public Sector Informatics Using AI: A Semantic Pattern Recognition Framework"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-0064-2424","authenticated-orcid":false,"given":"Marco","family":"Esperan\u00e7a","sequence":"first","affiliation":[{"name":"ISTAR, Instituto Universit\u00e1rio de Lisboa (ISCTE\u2014IUL), 1649-026 Lisboa, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-5903-2107","authenticated-orcid":false,"given":"Diogo","family":"Freitas","sequence":"additional","affiliation":[{"name":"ISTAR, Instituto Universit\u00e1rio de Lisboa (ISCTE\u2014IUL), 1649-026 Lisboa, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-6710-7773","authenticated-orcid":false,"given":"Pedro V.","family":"Paix\u00e3o","sequence":"additional","affiliation":[{"name":"Vision, Intelligence and Pattern Analysis Laboratory (VIPA), 9050-021 Funchal, Portugal"}]},{"given":"Tom\u00e1s A.","family":"Marcos","sequence":"additional","affiliation":[{"name":"Vision, Intelligence and Pattern Analysis Laboratory (VIPA), 9050-021 Funchal, Portugal"}]},{"given":"Rafael A.","family":"Martins","sequence":"additional","affiliation":[{"name":"Vision, Intelligence and Pattern Analysis Laboratory (VIPA), 9050-021 Funchal, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6662-0806","authenticated-orcid":false,"given":"Jo\u00e3o C.","family":"Ferreira","sequence":"additional","affiliation":[{"name":"ISTAR, Instituto Universit\u00e1rio de Lisboa (ISCTE\u2014IUL), 1649-026 Lisboa, Portugal"},{"name":"Faculty of Logistics, Molde University College, NO-6410 Molde, Norway"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e55721","DOI":"10.2196\/55721","article-title":"An Intelligent System for Classifying Patient Complaints Using Machine Learning and Natural Language Processing: Development and Validation Study","volume":"27","author":"Li","year":"2025","journal-title":"J. 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