Authors - Theresa T. Limos, Sheena Sapuay-Guillen Abstract - This study developed PU-Serv: A Tool in Analyzing Student Services Using Machine Learning, a web-based system designed to enhance the evaluation of student services through automated sentiment analysis. The study assessed the existing student services evaluation form in terms of adequacy, efficiency, and reliability and aimed to develop a machine learning–based model to support the analysis of student feedback.A descriptive and developmental research design guided by Agile methodology and the CRISP-DM framework was employed. Data were gathered from focus group discussions, questionnaires, and institutional student feedback records. Natural language processing techniques were used to preprocess narrative feedback, and the Support Vector Machine (SVM) algorithm was integrated into the system due to its high accuracy in sentiment classification. The developed PU-Serv system automatically analyzes student feedback and presents summarized results through a web-based dashboard. The system provides administrators with actionable insights that support data-driven decision-making, helping institutions identify service issues, improve responsiveness, and enhance the overall quality of student services.
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