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| Titre: | Application of large language models in generating Ukrainian corpora for training text classification systems |
| Auteur(s): | Nykytyuk, Vyacheslav Dolinskyi, Andrii |
| Affiliation: | Ternopil Ivan Puluj National Technical University, Ternopil, Department computer science, Ukraine |
| Bibliographic description (Ukraine): | Nykytyuk V. Application of large language models in generating Ukrainian corpora for training text classification systems / Vyacheslav Nykytyuk, Andrii Dolinskyi // Scientific Journal of TNTU. — Tern. : TNTU, 2026. — Vol 121. — No 1. — P. 46–55. |
| Bibliographic reference (2015): | Nykytyuk V., Dolinskyi A. Application of large language models in generating Ukrainian corpora for training text classification systems // Scientific Journal of TNTU, Ternopil. 2026. Vol 121. No 1. P. 46–55. |
| Bibliographic citation (APA): | Nykytyuk, V., & Dolinskyi, A. (2026). Application of large language models in generating Ukrainian corpora for training text classification systems. Scientific Journal of the Ternopil National Technical University;, 121(1), 46-55. TNTU.. |
| Bibliographic citation (CHICAGO): | Nykytyuk V., Dolinskyi A. (2026) Application of large language models in generating Ukrainian corpora for training text classification systems. Scientific Journal of the Ternopil National Technical University; (Tern.), vol. 121, no 1, pp. 46-55. |
| Is part of: | Вісник Тернопільського національного технічного університету, 1 (121), 2026 Scientific Journal of the Ternopil National Technical University;, 1 (121), 2026 |
| Journal/Collection: | Вісник Тернопільського національного технічного університету |
| Issue: | 1 |
| Volume: | 121 |
| Date de publication: | 24-mar-2026 |
| Submitted date: | 4-jan-2026 |
| Date of entry: | 13-oct-2026 |
| Editeur: | ТНТУ TNTU |
| Place of the edition/event: | Тернопіль Ternopil |
| DOI: | https://doi.org/10.33108/visnyk_tntu2026.01.046 |
| Mots-clés: | Artificial intelligence deep learning LSTM models text sentiment language dataset corpus text classification natural language processing Ukrainian language automation message analysis |
| Number of pages: | 10 |
| Page range: | 46-55 |
| Start page: | 46 |
| End page: | 55 |
| Résumé: | This study deals with the application of modern Artificial Intelligence technologies, mainly deep learning methods based on recurrent neural networks of the Long Short-Term Memory (LSTM) type, for the construction and analysis of Ukrainian-language corpora. The research attention is directed to the automated classification of text sentiment (tonality), which involves distributing texts into positive, neutral, and negative categories. This, sequentially, testified the effectiveness of the proposed algorithmic solutions and their suitability for the tasks of analyzing the emotional tone of Ukrainian-language content. |
| URI/URL: | http://elartu.tntu.edu.ua/handle/lib/54154 |
| ISSN: | 2522-4433 |
| Copyright owner: | © Ternopil Ivan Puluj National Technical University, 2026 |
| URL for reference material: | https://doi.org/10.36059/978-966-397-538-2-7 https://doi.org/10.48550/arXiv.2005.14165 https://doi.org/10.48550/arXiv.2303.18223 https://doi.org/10.33108/visnyk_tntu2023.01.072 https://doi.org/10.33108/visnyk_tntu2023.04.026 https://doi.org/10.37394/232014.2023.19.3 https://doi.org/10.48550/arXiv.1706.03762 https://doi.org/10.15407/jai2024.02.085 https://doi.org/10.48550/arXiv.2403.02990 https://doi.org/10.48550/arXiv.2302.13007 https://doi.org/10.1145/3544558 https://www.deeplearningbook.org https://doi.org/10.1162/neco.1997.9.8.1735 https://doi.org/10.48550/arXiv.1301.3781 https://doi.org/10.33395/sinkron.v8i2.12235 |
| References (International): | 1. O. Zalutska, Method for analyzing the Ukrainian language texts sentiment using natural language processing, in: Information Control Systems and Intelligent Technologies. Advances and Applications, Liha-Pres, Lviv, 2022, pp. 122–137. https://doi.org/10.36059/978-966-397-538-2-7. (In Ukrainian). 2. A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, I. Sutskever, Language Models are Few-Shot Learners, (2020). https://doi.org/10.48550/arXiv.2005.14165 (Accessed 15 October 2025). 3. T. Chen, Y. Chen, T. Gui, Q. Huang, L. Xue, Qi. Zhang, A Survey on Large Language Models in Natural Language Processing, (2023). https://doi.org/10.48550/arXiv.2303.18223 (Accessed: 15 October 2025). 4. R. Khrabatyn, V. Zaiats, Technologies for designing the structure of the information system for monitoring the technical condition of bridge structures, Scientific Journal of the Ternopil National Technical University. 109 (2023) 72–79. https://doi.org/10.33108/visnyk_tntu2023.01.072. (In Ukrainian). 5. O.A. Pastukh, O.V. Tkach, Brain-computer interaction based on motor imagery using machine learning, Scientific Journal of the Ternopil National Technical University,. 112 (2023), 26–31. https://doi.org/10.33108/visnyk_tntu2023.04.026. (In Ukrainian). 6. F. Li, C. Cui, Y. Hu, L. Wang, Sentiment Analysis of User Comment Text based on LSTM, WSEAS Transactions on Signal Processing. 19 (2023) 19–31. https://doi.org/10.37394/232014.2023.19.3. 7. A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin, Attention Is All You Need, (2017). https://doi.org/10.48550/arXiv.1706.03762 (Accessed: 15 October 2025). 8. M. Prytula, Fine-tuning BERT, DistilBERT, XLM-RoBERTa and Ukr-RoBERTa models for sentiment analysis of Ukrainian language reviews, Artificial Intelligence, 2 (2024), 85–97. https://doi.org/10.15407/jai2024.02.085. (In Ukrainian). 9. B. Ding, J. Zhou, Z. Li, X. Long, X. Li, Z. Wu, S. Gao, Data Augmentation using LLMs: Data Perspectives, Learning Paradigms and Challenges, (2024). https://doi.org/10.48550/arXiv.2403.02990 (Accessed: 15 October 2025). 10. H. Dai, Z. Liu, W. Liao, X. Huang, Y. Cao, Z. Wu, S. Zhao, W. Zhu, S. Wei, T. Liu, N.S. Peng, AugGPT: Leveraging ChatGPT for Text Data Augmentation, (2023). https://doi.org/10.48550/arXiv.2302.13007 (Accessed: 15 October 2025). 11. M. Bayer, M.A. Kaufhold, C. Reuter, A Survey on Data Augmentation for Text Classification, ACM Computing Surveys., 55 (2023), 1–39. https://doi.org/10.1145/3544558. 12.I. Goodfellow, Y. Bengio, A. Courville, Deep Learning, MIT Press, Cambridge, 2016. https://www.deeplearningbook.org. 13. S. Hochreiter, J. Schmidhuber, Long Short-Term Memory, Neural Computation,. 9 (1997), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735. 14. T. Mikolov, K. Chen, G. Corrado, J. Dean, Efficient Estimation of Word Representations in Vector Space, (2013). https://doi.org/10.48550/arXiv.1301.3781 (Accessed: 15 October 2025). 15. A. Simarmata, Anthony, Tiffany, M. Phanie, Sentiment Analysis On Twitter Posts About The Russia and Ukraine War With Long Short-Term Memory, Sinkron, 8 (2023), 762–772. https://doi.org/10.33395/sinkron.v8i2.12235. |
| Content type: | Article |
| Collection(s) : | Вісник ТНТУ, 2026, № 1 (121) |
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