Evaluation of transformer-based models for sentiment analysis in Bahasa Malaysia

Ahmad Radzi, Syafeeza and Zulkalnain, Mohd Asyraf and Mohd Saad, Wira Hidayat and Rahaman, Shahid (2025) Evaluation of transformer-based models for sentiment analysis in Bahasa Malaysia. Journal Of Telecommunication, Electronic And Computer Engineering (JTEC), 17 (1). pp. 29-33. ISSN 2180-1843

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Abstract

This study investigates the application of advanced Transformer-based models, namely BERT, DistilBERT, BERT-multilingual, ALBERT, and BERT-CNN, for sentiment analysis in Bahasa Malaysia, addressing unique challenges such as mixed-language usage and abbreviated expressions in social media text. Using the Malaya dataset to ensure linguistic diversity and domain coverage, the research incorporates robust preprocessing techniques, including synonym mapping and sentiment-aware tokenization, to enhance feature extraction. Through rigorous evaluation, BERT-CNN exhibits the best accuracy (96.3%), followed by BERT-multilingual (89.84%) and BERT (89.5%). DistilBERT and ALBERT delivered competitive performance (88.96% and 88.76%, respectively) while offering reduced computational requirements, highlighting the trade-offs between performance and efficiency. The study emphasizes optimized strategies for handling challenges in positive sentiment classification and demonstrates the efficacy of transformer architectures in nuanced sentiment detection for low-resource languages. These findings contribute to advancing Natural Language Processing (NLP) for scalable sentiment analysis across domains.

Item Type:	Article
Uncontrolled Keywords:	Transformer-based models, Sentiment analysis, Bahasa Malaysia, Natural Language Processing
Divisions:	Faculty Of Electronics And Computer Technology And Engineering
Depositing User:	Norfaradilla Idayu Ab. Ghafar
Date Deposited:	21 Jul 2026 03:57
Last Modified:	21 Jul 2026 03:57
URI:	http://eprints.utem.edu.my/id/eprint/30176
Statistic Details:	View Download Statistic

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