1Department of Computer Science and Information Technology, Baze University, Nigeria
2Faculty of Natural and Applied Science, Nasarawa State University, Nigeria
3Economic Community of West African State, Nigeria
4 Department of Computer Science and Engineering, GD Goenka University, India
BibTex Citation Data :
@article{JMASIF81053, author = {Nurudeen Jibrin and Gilbert Aimufua and Okorie Sunday Onyedikachi and Alegbe Adesola Anthony and Ugbai Solomon Chukwunwike and Fadila Dantalle Aliyu}, title = {Parameter-Efficient Few-Shot Sentiment Analysis Using LoRA-Enhanced Transformers}, journal = {Jurnal Masyarakat Informatika}, volume = {17}, number = {1}, year = {2026}, keywords = {Low-Rank Adaptation; Sentiment Analysis; Hausa Language; Natural Language Processing; Few-Shot Learning}, abstract = { Sentiment analysis in low-resource languages is often limited by scarce annotated data and the high computational cost of fine-tuning large language models. This study proposes a parameter-efficient framework that integrates Low-Rank Adaptation (LoRA) with lightweight transformer architectures, including AfriBERTa, DistilBERT, and MiniLMv2, for Hausa sentiment analysis using the NaijaSenti dataset. The framework is designed to address three key challenges: effective few-shot learning, robustness under extreme data scarcity, and mitigation of language-specific linguistic errors. Experimental results demonstrate that AfriBERTa-LoRA achieves 69.0% accuracy, only 4.8 percentage points below a fully fine-tuned XLM-RoBERTa baseline, while utilizing just 1.06% of trainable parameters and reducing GPU memory consumption by approximately 50%. Performance improves consistently with increasing data, indicating strong scalability under few-shot conditions. Linguistic error analysis reveals four dominant Hausa-specific failure modes accounting for 71.5% of misclassifications. Targeted mitigation strategies yield an 8.7 percentage point reduction in error rate (28% relative reduction, p < 0.01), with each individual strategy demonstrating statistical significance. These findings establish LoRA as an effective and efficient paradigm for low-resource natural language processing, providing a scalable and reproducible framework for sentiment analysis in underrepresented African languages. }, issn = {2777-0648}, pages = {146--159} doi = {10.14710/jmasif.17.1.81053}, url = {https://ejournal.undip.ac.id/index.php/jmasif/article/view/81053} }
Refworks Citation Data :
Sentiment analysis in low-resource languages is often limited by scarce annotated data and the high computational cost of fine-tuning large language models. This study proposes a parameter-efficient framework that integrates Low-Rank Adaptation (LoRA) with lightweight transformer architectures, including AfriBERTa, DistilBERT, and MiniLMv2, for Hausa sentiment analysis using the NaijaSenti dataset. The framework is designed to address three key challenges: effective few-shot learning, robustness under extreme data scarcity, and mitigation of language-specific linguistic errors. Experimental results demonstrate that AfriBERTa-LoRA achieves 69.0% accuracy, only 4.8 percentage points below a fully fine-tuned XLM-RoBERTa baseline, while utilizing just 1.06% of trainable parameters and reducing GPU memory consumption by approximately 50%. Performance improves consistently with increasing data, indicating strong scalability under few-shot conditions. Linguistic error analysis reveals four dominant Hausa-specific failure modes accounting for 71.5% of misclassifications. Targeted mitigation strategies yield an 8.7 percentage point reduction in error rate (28% relative reduction, p < 0.01), with each individual strategy demonstrating statistical significance. These findings establish LoRA as an effective and efficient paradigm for low-resource natural language processing, providing a scalable and reproducible framework for sentiment analysis in underrepresented African languages.
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Last update: 2026-05-20 03:23:53
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