Privacy-Aware XAI for Opinion Mining in Healthcare and E-Governance

Digital healthcare and e-governance platforms have become central to service delivery, citizen engagement, and policy implementation. These platforms generate vast volumes of unstructured textual data in the form of patient feedback, citizen complaints, online reviews, and public opinions. Analysing this data provides valuable insights into service quality, institutional trust, and public perception. Opinion mining and sentiment analysis techniques enable organisations to systematically extract such insights. While these techniques have been successfully applied in commercial domains, their use in sensitive public domains raises significant concerns related to data privacy, transparency, and ethical accountability. Existing systems often prioritise predictive accuracy while neglecting explainability and privacy protection, limiting their suitability for healthcare and public-sector decision-making. This project proposes a privacy-aware and explainable artificial intelligence framework for opinion mining, specifically tailored for digital healthcare and e-governance systems. By integrating sentiment analysis with privacy-preserving mechanisms and explainable AI techniques, the project aims to deliver trustworthy, interpretable, and ethically aligned analytical insights that support responsible decision-making.

Faculty Supervisor:

Ernest Johnson

Student:

Partner:

SRM Institute of Science and Technology

Discipline:

Computer science

Sector:

Health and Related Sciences and Technology; Technology

University:

University of Regina

Program: