Fine-Tuning Transformers for Multi-Label Emotion Classification: Experiments on Kazakh News Texts
Yelibayeva G. Bolatbekkyzy K. Strecker M.
2025Institute of Electrical and Electronics Engineers Inc.
International Conference on Computer Science and Engineering, UBMK
2025Issue 2025299 - 304 pp.
This paper presents a multi-task learning approach for multi-label emotion classification in Kazakh-language news articles using transformer-based models. We constructed a labeled dataset of 600 news articles by collecting and analyzing real user reactions from the official Kazakh news platform NUR.KZ. Each article was annotated with one or more emotions based on aggregated user feedback. A pre-trained transformer model was fine-tuned to predict these emotional reactions, demonstrating effective performance in detecting multiple emotions simultaneously. Our model achieved low loss values during training and validation, indicating good convergence, and high accuracy across the emotion categories. These results highlight the effectiveness of using naturally occurring user feedback for emotion modeling in low-resource languages and suggest promising applications in other domains such as social media and public opinion analysis.
Kazakh language , low-resource NLP , multi-label classification , multi-task learning , news articles , transformers , user reactions
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L.N. Gumilyov Eurasian National University, Department of Artificial Intelligence Technologies, Astana, Kazakhstan
Université de Toulouse, IRIT, Toulouse, France
L.N. Gumilyov Eurasian National University
Université de Toulouse
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