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

This study explores transformer-based models such as BERT, mBERT, and XLM-R for multilingual 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.