Iip Permana, Hidayatul Fajri, Zikri Alhadi, Yeka Hendriyani, Yuliarti, Artha Dini Akmal
Sentiment analysis in regional languages remains a critical challenge in low-resource natural language processing (NLP). The Minang language, spoken by over eight million people in Indonesia, is underrepresented in NLP research. This study evaluates the performance of IndoBERT - a pre-trained transformer model for Bahasa Indonesia - on sentiment classification tasks in the Minang language. A dataset of 5,000 tweets containing parallel Indonesian and Minang texts is used for fine-tuning IndoBERT and benchmarking it against traditional methods such as Bag-of-Words and TF-IDF, as well as the multilingual BERT (mBERT) model. Experimental results show that IndoBERT achieves the highest overall accuracy (0.870) and F1-score (0.840), outperforming baseline models across multiple evaluation metrics. The findings demonstrate IndoBERT effectiveness for sentiment analysis in morphologically rich, low-resource languages and highlight the potential of transfer learning from a national to a regional language. This work contributes to advancing regional language NLP and establishes a benchmark for future research in Minang-language sentiment modelling. © 2025 IEEE.
Universitas Negeri Padang, Department of Public Administration, Padang, Indonesia; Universitas Negeri Padang, Department of Electronics, Padang, Indonesia
