LRSA-XLMR-Sentiment-ID

LRSA-XLMR-Sentiment-ID

Model Description

LRSA-XLMR-Sentiment-ID is a fine-tuned XLM-RoBERTa (XLM-R) model for Indonesian sentiment analysis developed as part of the study:

Stress-Testing Indonesian Sentiment Classification under Weak Supervision, Domain Generalization, and Character-Level Perturbations

The model was trained and evaluated on a large Indonesian sentiment corpus containing approximately 75,000 samples collected from social media and news domains.

Task

Sentiment Classification

Labels:

• Negative
• Neutral
• Positive

Dataset

Sources:

• YouTube comments
• Indonesian news articles and comments

Domains:

• Politics
• Economics
• Social issues
• Public policy

Statistical Significance

McNemar testing demonstrated statistically significant differences between XLM-R and the baseline SVM model (p < 0.001), confirming the effectiveness of transformer-based architectures under low-resource Indonesian sentiment classification settings.

Repository

Code, experimental results, figures, and supplementary materials:

https://github.com/mziarehman4353/LRSA-LLM

Authors

Zia Ul Rehman Zafar
Dedi Gunawan
Muhammad Nouman

Department of Informatics Engineering
Universitas Muhammadiyah Surakarta, Indonesia

Citation

If you use this model, please cite:

Zafar, Z. U. R., Gunawan, D., Nouman, M.

Stress-Testing Indonesian Sentiment Classification under Weak Supervision, Domain Generalization, and Character-Level Perturbations.

License

MIT License