We propose a hybrid approach for multilingual sentiment analysis that combines extractive and abstractive summarization to address the limitations of standalone methods. The model integrates TF-IDF-based extraction with a fine-tuned XLM-R abstractive module, enhanced through dynamic thresholding and cultural adaptation. Experiments across 10 languages demonstrate significant improvements over baselines, achieving an accuracy of 0.90 for English and 0.84 for low-resource languages. The approach also achieves 22% greater computational efficiency compared to traditional methods. Practical applications include real-time brand monitoring and cross-cultural discourse analysis. Future work will focus on optimizing performance for low-resource languages through 8-bit quantization.
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Mikhail Krasitskii
Grigori Sidorov
Olga Kolesnikova
International Journal of Combinatorial Optimization Problems and Informatics.
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Krasitskii et al. (Sun,) studied this question.
www.synapsesocial.com/papers/68ecfebf950606aabec0963d — DOI: https://doi.org/10.61467/2007.1558.2025.v16i4.1185
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