Comparative Approaches to Sentiment Analysis Using Datasets in Major European and Arabic Languages

Mikhail Krasitskii, Olga Kolesnikova, Liliana Chanona Hernandez, Grigori Sidorov, Alexander Gelbukh
1/21/2025
cs.CL

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Abstract

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 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.

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Cite this paper

@article{krasitskii2025comparative,
  title  = {Comparative Approaches to Sentiment Analysis Using Datasets in Major European and Arabic Languages},
  author = {Mikhail Krasitskii and Olga Kolesnikova and Liliana Chanona Hernandez and Grigori Sidorov and Alexander Gelbukh},
  year   = {2025},
  eprint = {2501.12540},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2501.12540v1}
}

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