Mining Social Media for Data-Driven Analysis of Educational Data with Machine Learning

The Philippine Republic Act 10931, otherwise known as the Universal Access to Quality Tertiary Education (UAQTE) Act aims to provide free higher education opportunities and subsidies to undergraduate students. Despite the recent increase in its national budget allocation, there remains uncertainty on its effectiveness in the perspective of its stakeholders. In this study, we performed topic modeling analysis using Latent Dirichlet Allocation on a corpus of 7,754 preprocessed texts sourced from Facebook and Twitter. Among the identified themes, Clientele Satisfaction stands out as the dominant topic, followed by Information and Inquiries, Social Media Presence, Gratitude and Appreciation, and Educational Opportunities. The emerging dominant topics per region conveyed expressions of gratitude and appreciation for the support provided by the program. However, based on the analysis, the policymakers and program implementers should consider improving the dissemination of information to their stakeholders. This study offers insights into public perceptions, emphasizing the programs' impacts and areas necessitating improvements.