TY - GEN
T1 - Exploring Sentiment Analysis in Tigrigna
T2 - 14th National CCF Conference on Natural Language Processing and Chinese Computing, NLPCC 2025
AU - Gebremeskel, Hagos Gebremedhin
AU - Feng, Chong
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.
PY - 2026
Y1 - 2026
N2 - This study explores sentiment analysis for Tigrigna social media texts, a low-resource language with limited computational tools. We collected and annotated a dataset of Tigrigna posts and comments from social media platforms and evaluated several machine learning and deep learning models, including Naive Bayes, SVM, LSTM, and XLM-RoBERTa. Results show that XLM-RoBERTa, a transformer-based model, achieved the highest performance with an F1-score of 0.83, effectively handling Tigrigna’s complex morphology and cultural nuances. Key challenges included data scarcity, dialectal variations, and idiomatic expressions unique to Tigrigna. Future work suggests expanding Tigrigna resources, exploring optimized models for resource-constrained environments, and developing applications for real-world sentiment monitoring. This research contributes to advancing NLP for low-resource languages, promoting inclusivity in sentiment analysis.
AB - This study explores sentiment analysis for Tigrigna social media texts, a low-resource language with limited computational tools. We collected and annotated a dataset of Tigrigna posts and comments from social media platforms and evaluated several machine learning and deep learning models, including Naive Bayes, SVM, LSTM, and XLM-RoBERTa. Results show that XLM-RoBERTa, a transformer-based model, achieved the highest performance with an F1-score of 0.83, effectively handling Tigrigna’s complex morphology and cultural nuances. Key challenges included data scarcity, dialectal variations, and idiomatic expressions unique to Tigrigna. Future work suggests expanding Tigrigna resources, exploring optimized models for resource-constrained environments, and developing applications for real-world sentiment monitoring. This research contributes to advancing NLP for low-resource languages, promoting inclusivity in sentiment analysis.
KW - cross-linguistic transfer learning
KW - Large Language Models
KW - low-resource languages
KW - sentiment analysis
KW - Tigrigna social media
UR - https://www.scopus.com/pages/publications/105025912914
U2 - 10.1007/978-981-95-3349-7_34
DO - 10.1007/978-981-95-3349-7_34
M3 - Conference contribution
AN - SCOPUS:105025912914
SN - 9789819533480
T3 - Lecture Notes in Computer Science
SP - 442
EP - 455
BT - Natural Language Processing and Chinese Computing - 14th National CCF Conference, NLPCC 2025, Proceedings
A2 - Mao, Xian-Ling
A2 - Ren, Zhaochun
A2 - Yang, Muyun
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 7 August 2025 through 9 August 2025
ER -