SENTIMENT ANALYSIS OF NON-ENGLISH TEXT BASED ON NLP TECHNOLOGIES
Keywords:
sentiment analysis, machine translation, natural language processingAbstract
The paper addresses sentiment analysis techniques for texts in languages other than English using natural language processing (NLP) technologies, using Ukrainian texts as an example. A system for classifying the sentiment of Ukrainian news headlines using deep learning methods is presented and two LSTM-based neural network models are developed: one trained on a small manually-labeled Ukrainian text corpus, and another trained on a larger Twitter-based English corpus that was machine-translated into Ukrainian. A comparative evaluation of these models’ performance is conducted, and observations are made about language-specific challenges in sentiment expression for Ukrainian. The advantages and limitations of each approach are discussed and directions for further research in multilingual sentiment analysis are outlined.
References
Kanishcheva, O., Bobicev, V. Good News vs. Bad News: What Are They Talking About? // Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2017). – 2017. [Electronic resource] – Access mode: https://aclanthology.org/R17-1044
Zalutska, O., Molchanova, M., Sobko, O., Mazurets, O., Pasichnyk, O., Barmak, O., Krak, I. Method for Sentiment Analysis of Ukrainian-Language Reviews in E-Commerce Using RoBERTa Neural Network // Proceedings of the 7th International Conference on Computational Linguistics and Intelligent Systems (COLINS 2023), Kharkiv, Ukraine. – 2023 [Electronic resource] – Access mode: https://ceur-ws.org/Vol-3387/paper26.pdf
Ponti, T., Bharadwaj, P., et al. A Systematic Review of Cross-Lingual Sentiment Analysis: Methods and Challenges // Artificial Intelligence Review. – 2020. [Electronic resource] – Access mode: https://dl.acm.org/doi/10.1145/3645106
