Authors - Kent Cyryl A. Campit, Christian Kelvin Gonzales Abstract - This study explored a data-driven approach to evaluating citizen feedback within the Provincial Government of La Union (PGLU) by integrating quantitative and qualitative analytical techniques. Traditional feedback systems in government offices often rely on averages and summary reports, limiting the ability to capture deeper citizen experiences and concerns. To address this gap, the research transformed paper-based feedback forms into a structured digital dataset covering responses from 34 frontline offices and service units from July 2025 to January 2026. The study applied Customer Satisfaction Score (CSAT), Weighted Mean, and Range of Interval to measure and classify service performance levels. For qualitative analysis, Latent Dirichlet Allocation (LDA) was used to identify recurring themes in open-ended responses, while a dual-model sentiment analysis approach combining VADER and RoBERTa classified citizen feedback into positive, neutral, and negative sentiments. The analytical pro-cesses were implemented using Microsoft Excel, Google Sheets, and Python through Google Colaboratory. Findings revealed consistently high satisfaction ratings across offices, while qualitative analysis uncovered recurring themes related to service efficiency, staff assistance, facility conditions, and operational concerns. RoBERTa demonstrated better contextual understanding and achieved higher performance metrics compared to VADER. The study further developed an Observed Satisfaction Classification Framework to support evidence-based decision-making and service improvement. Ultimately, the re-search demonstrated how citizen feedback can be transformed into actionable governance insights that promote transparency, accountability, and continuous improvement in public service delivery, aligned with Sustainable Development Goal 16.
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