Abstract: Philippine Higher Education Institutions (HEIs) integrate social media (SM) platform like Facebook to digitally market their position and brand, post announcements, news and other pertinent information about their institution. Though SM is used in education, it is noted that its primary purpose is for users’ online social engagement. SM users react to posts, comment and give their sentiments on a particular topic to discuss and share their daily life in an informal and casual manner. Students even create unofficial Facebook pages associated to their institution and post their experiences and sentiments. These digital footprints of SM users could be a good source to use Data Mining (DM) techniques to understand HEI stakeholders’ experiences and form decision-making interventions to improve quality education. The main objective of this study is mining social media data to support Philippine HEIs identify their stakeholders’ SM behaviors and make proactive and knowledge-driven decisions. Specifically, the objectives of this study are: to develop a workflow of analyzing social media data; to use Naïve Bayes Classifier in identifying the SM behaviors of Philippine HEI stakeholders; and to interpret the results of DM and propose intervention measures to improve quality education. Using Agile Methodology CRISP-DM model, this study centers on mining users’ post and comments in Philippine HEI Facebook pages.
Keywords: Data Mining, Social Media, Higher Education Institutions, Philippines, Naïve Bayes.
Title: Mining Social Media Data of Philippine Higher Education Institutions Using Naïve Bayes Classifier Algorithm
Author: Joey Aviles, Rosanna Esquivel
International Journal of Computer Science and Information Technology Research
ISSN 2348-1196 (print), ISSN 2348-120X (online)
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