Proactive Complaint Management in Public Sector Informatics Using AI: A Semantic Pattern Recognition Framework

Proactive Complaint Management in Public Sector Informatics Using AI: A Semantic Pattern Recognition Framework

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 sim...

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Bibliographic Details
Main Authors: Marco Esperança, Diogo Freitas, Pedro V. Paixão, Tomás A. Marcos, Rafael A. Martins, João C. Ferreira
Format: Article
Language:English
Published: MDPI AG 2025-06-01
Series:Applied Sciences
Subjects:
artificial intelligence
public sector
complaint management
semantic clustering
predictive analytics
digital transformation
Online Access:https://www.mdpi.com/2076-3417/15/12/6673
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Summary: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.
ISSN:2076-3417

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