Comparative Approaches to Sentiment Analysis Using Datasets in Major European and Arabic Languages
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866917898795089920 |
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| author | Krasitskii, Mikhail Kolesnikova, Olga Hernandez, Liliana Chanona Sidorov, Grigori Gelbukh, Alexander |
| author_facet | Krasitskii, Mikhail Kolesnikova, Olga Hernandez, Liliana Chanona Sidorov, Grigori Gelbukh, Alexander |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_12540 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Comparative Approaches to Sentiment Analysis Using Datasets in Major European and Arabic Languages Krasitskii, Mikhail Kolesnikova, Olga Hernandez, Liliana Chanona Sidorov, Grigori Gelbukh, Alexander Computation and Language 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. |
| title | Comparative Approaches to Sentiment Analysis Using Datasets in Major European and Arabic Languages |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2501.12540 |