TIGSEN: Building a Low-Resource Dataset and Benchmarking for Tigrigna Sentiment Analysis with Cross-Lingual Transfer Learning Approaches

Sentiment analysis in low-resource languages faces several challenges. This paper addresses the challenges of extremely low-resource and methodologically understudied sentiment analysis for Tigrigna, an official language spoken in Eritrea and the Tigray region of Ethiopia. We introduce TIGSEN, the first large-scale, multi-domain benchmark dataset for Tigrigna sentiment analysis, comprising 68,596 annotated text samples from official social media, news, and review forums. Created through a rigorous native-speakers annotation protocol, the dataset is designed to enable robust model training and evaluation. To prove its utility and to establish a strong, reproducible baseline for the community, we propose and evaluate a systematic cross-transfer learning framework. This methodology deliberately leverages annotated data from high-resource to linguistically related languages, English and Amharic, to overcome the limitations of Tigrigna’s small data pool. Our experiments show that models fine-tuned directly on TIGSEN achieved competitive performance. At the same time, the proposed cross-transfer framework yields a significant performance gain, achieving an accuracy of 87.6% over a strong multilingual baseline. This result validates TIGSEN as a learnable and challenging benchmark and provides a practical blueprint for resource amplification in low-resource settings. We publicly release the TIGSEN dataset, annotation guidelines, and benchmarking code to serve as a foundational resource and a catalyst for future LLM research in Tigrigna NLP.