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

arXiv:2501.12540v1 Announce Type: new
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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