ANWENDUNG MODERNER SPRACHMODELLE FÜR MULTICLASS-SENTIMENT-ANALYSE BEI CODE-SWITCHING

Автор(и)

  • K. D. Hiloviants Nationale Technische Universität der Ukraine "Kyjiwer Igor-Sikorsky Polytechnisches Institut", Україна
  • S. M. Ivanenko Nationale Technische Universität der Ukraine "Kyjiwer Igor-Sikorsky Polytechnisches Institut", Україна
  • N. V. Shapoval Nationale Technische Universität der Ukraine "Kyjiwer Igor-Sikorsky Polytechnisches Institut", Україна

DOI:

https://doi.org/10.20535/IWPOK3.2025.art.21

Анотація

Die Sentiment-Analyse ist eine Methode der Verarbeitung natürlicher Sprache (Natural Language Processing, NLP), die zur automatisierten Erkennung emotional gefärbter Lexik in Texten und zur Bestimmung der emotionalen Bewertung des Autors in Bezug auf die im Text beschriebenen Objekte entwickelt wurde. Die Sentiment-Analyse gehört zu den Kernaufgaben des NLP.

Посилання

Aryal, S. K., Prioleau, H., & Washington, G. (2022). Sentiment Classification of Code-Switched Text using Pre-trained Multilingual Embeddings and Segmentation. ArXiv.org. https://arxiv.org/abs/2210.16461

Bobichev, V., Kanishcheva, O., & Cherednichenko, O. (2017, May 1). Sentiment analysis in the Ukrainian and Russian news. IEEE Xplore. https://doi.org/10.1109/UKRCON.2017.8100410

Chen, L., Shang, S., & Wang, Y. (2025). Bridging resource gaps in crosslingual sentiment analysis: adaptive self-alignment with data augmentation and transfer learning. PeerJ Computer Science, 11, e2851. https://doi.org/10.7717/peerj-cs.2851

Conneau, A., Khandelwal, K., Goyal, N., Chaudhary, V., Wenzek, G., Guzmán, F., Grave, E., Ott, M., Zettlemoyer, L., & Stoyanov, V. (2020). Unsupervised Cross-lingual Representation Learning at Scale. ArXiv:1911.02116 [Cs]. https://arxiv.org/abs/1911.02116

Dettmers, T., Pagnoni, A., Holtzman, A., & Zettlemoyer, L. (2023). QLoRA: Efficient Finetuning of Quantized LLMs. https://doi.org/10.48550/arxiv.2305.14314

Gladys, A.A., & Vetriselvi, V. (2024). Sentiment analysis on a lowresource language dataset using multimodal representation learning and cross-lingual transfer learning. Applied Soft Computing, 157, 111553. https://doi.org/10.1016/j.asoc.2024.111553

Haltiuk, M., & Smywiński-Pohl, A. (2025). On the Path to Make Ukrainian a High-Resource Language. Proceedings of the Fourth Ukrainian Natural Language Processing Workshop (UNLP 2025), 120–130. https://doi.org/10.18653/v1/2025.unlp-1.14

Hu, E. J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., & Chen, W. (2021). LoRA: Low-Rank Adaptation of Large Language Models. ArXiv:2106.09685 [Cs]. https://arxiv.org/abs/2106.09685

Koto, F., Beck, T., Talat, Z., Gurevych, I., & Baldwin, T. (2024). Zero- shot Sentiment Analysis in Low-Resource Languages Using a Multilingual Sentiment Lexicon. ArXiv.org. https://arxiv.org/abs/2402.02113

Lashyn, Y., Trofymchuk, O., Zabolotnyi, S., Voitko, O., & Seabra, E. (2025). SENTIMENT ANALYSIS OF TEXTS USING RECURRENT NEURAL NETWORKS OF THE TRANSFORMER ARCHITECTURE. Advanced Information Systems, 9(3), 91–101. https://doi.org/10.20998/2522-9052.2025.3.11

Prytula, M. (2024). Fine-tuning BERT, DistilBERT, XLM-RoBERTa and Ukr-RoBERTa models for sentiment analysis of ukrainian language reviews. Jai.in.ua. https://jai.in.ua/index.php/en/issues?paper_num=1623

Romanyshyn, M. (2013). Rule-Based Sentiment Analysis of Ukrainian Reviews. International Journal of Artificial Intelligence & Applications, 4(4), 103–111. https://doi.org/10.5121/ijaia.2013.4410

Shynkarov, Y., Solopova, V., & Schmitt, V. (2025). Improving Sentiment Analysis for Ukrainian Social Media Code-Switching Data. Proceedings of the Fourth Ukrainian Natural Language Processing Workshop (UNLP 2025), 179–193. https://doi.org/10.18653/v1/2025.unlp-1.18

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Опубліковано

2025-12-30