Mikhail Krasitskii, Olga Kolesnikova, Liliana Chanona Hernandez, Grigori Sidorov and Alexander Gelbukh, Instituto Politécnico Nacional, México

This study explores transformer-based models such as BERT, mBERT, and XLM-R for multi-lingual sentiment analysis across diverse linguistic structures. Key contributions include the identification of XLM-R’s superior adaptability in morphologically complex languages, achieving accuracy levels above 88%. The work highlights fine-tuning strategies and emphasizes their significance for improving sentiment classification in underrepresented languages.

Natural language processing, Sentiment analysis, Multilingual models