Mining Social Media Data of Philippine Higher Education Institutions Using Naïve Bayes Classifier Algorithm
Abstract— Higher Education Institutions in the Philippines integrate social media like Facebook to market
their brand, give some announcements, update news and other important information about their institution.
However, their stakeholders create unofficial Facebook pages associated to their institution. These pages are
areas for students mostly to react to posts, comment, give their opinion on a given topic and share their daily
life online in an informal and casual way. These social media footprints were used in the data mining process
to identify their discourse. With this, Universities and Colleges could form decision-making mediations and
improve the quality of education and service they provide. The specific objectives of this study are: to create
a workflow on how to analyze social media data; to use Naïve Bayes Algorithm in classifying the discourse
of Philippine HEI stakeholders; and to deduce the results of data mining and recommend intervention
activities to improve quality education. Knowledge Discovery Databases (KDD) was used in the study. The
result of the data mining process is displayed through a word cloud and a dynamic graph visualizing the
classifications of posts and comments. Social engagement, academics, emotions, health, policies and finances
were identified as the SM discourse of Philippine HEI Stakeholders.
Index Terms— data mining, social media, higher education institutions, Philippines, naïve bayes
Joey S. Aviles
Panpacific University North Philippines, Tayug Campus Inc., PHILIPPINES
Rosanna A. Esquivel
Angeles University Foundation, PHILIPPINES
Cite: Joey S. Aviles, Rosanna A. Esquivel, "Mining Social Media Data of Philippine Higher Education Institutions Using Naïve Bayes Classifier Algorithm," Proceedings of 2019 the 9th International Workshop on Computer Science and Engineering, pp. 681-688, Hong Kong, 15-17 June, 2019.