Comparative Approaches to Sentiment Analysis Using Datasets in Major European and Arabic Languages
It improves sentiment classification for underrepresented languages, but is incremental in applying existing methods to new data.
This study tackled sentiment analysis across European and Arabic languages using transformer models, finding that XLM-R achieved over 88% accuracy, especially in morphologically complex languages.
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.