Sentiment Analysis of Code-mixed Social Media Data on Philippine UAQTE using Fine-tuned mBERT Model

• Published in: International journal of advanced computer science & applications Vol. 14; no. 7
• Main Authors: Maceda, Lany L., Satuito, Arlene A., Abisado, Mideth B.
• Format: Journal Article
• Language: English
• Published: West Yorkshire Science and Information (SAI) Organization Limited 2023
• Subjects: Coders; Data mining; Digital media; Education; Effectiveness; Model accuracy; Sentiment analysis; Social networks; Subsidies; Philippines
• ISSN: 2158-107X; 2156-5570
• DOI: 10.14569/IJACSA.2023.0140777

The Universal Access to Quality Tertiary Education (UAQTE) marks a significant policy change in the Philippines. While the program’s objective is to offer free higher education and tertiary education subsidies to eligible Filipino students, its viability and effectiveness have been subject to scrutiny and continuous evaluation. This study explores the sentiments of Filipinos towards UAQTE. Leveraging a fine-tuned multilingual Bidirectional Encoder Representations from Transformers (mBERT) model, we conducted sentiment analysis on code-mixed data. With minimal preprocessing, our model achieved an accuracy of 80.21% and an F1 score of 81.14%, surpassing previous related studies and confirming its effectiveness in handling code-mixed data. The results reveal that the majority of social media users view UAQTE positively or beneficially. However, negative sentiments highlight concerns related to subsidy delays, alleged fund misuse, and application challenges. Additionally, neutral sentiments center around subsidy-related announcements. These findings provide valuable insights for its key stakeholders involved in the implementation, enhancement, and evaluation of UAQTE.