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            <namePart type="given">Mika</namePart>
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    <abstract>This paper introduces a novel hybrid architecture for multilingual sentiment analysis specifically designed for morphologically complex Uralic languages. Our approach synergistically combines extractive and abstractive summarization with specialized morphological processing for agglutinative structures. The proposed model integrates dynamic thresholding mechanisms and culturally-aware attention layers, achieving statistically significant improvements of 12% accuracy for Uralic languages (p \ensuremath&lt; 0.01) while outperforming state-of-the-art alternatives in summarization quality (ROUGE 1: 0.60 vs. 0.52). Key innovations include language-specific stemmers for Finno-Ugric languages and cross-Uralic transfer learning, yielding 15.7% improvement in recall while maintaining 98.2% precision. Comprehensive evaluations across multiple datasets demonstrate consistent superiority over contemporary baselines, with particular emphasis on addressing Uralic language processing challenges.</abstract>
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%0 Conference Proceedings
%T A Hybrid Multilingual Approach to Sentiment Analysis for Uralic and Low-Resource Languages: Combining Extractive and Abstractive Techniques
%A Krasitskii, Mikhail
%A Kolesnikova, Olga
%A Sidorov, Grigori
%A Gelbukh, Alexander
%Y Hämäläinen, Mika
%Y Rießler, Michael
%Y Morooka, Eiaki V.
%Y Kharlashkin, Lev
%S Proceedings of the 10th International Workshop on Computational Linguistics for Uralic Languages
%D 2025
%8 December
%I Association for Computational Linguistics
%C Joensuu, Finland
%@ 979-8-89176-360-9
%F krasitskii-etal-2025-hybrid
%X This paper introduces a novel hybrid architecture for multilingual sentiment analysis specifically designed for morphologically complex Uralic languages. Our approach synergistically combines extractive and abstractive summarization with specialized morphological processing for agglutinative structures. The proposed model integrates dynamic thresholding mechanisms and culturally-aware attention layers, achieving statistically significant improvements of 12% accuracy for Uralic languages (p \ensuremath< 0.01) while outperforming state-of-the-art alternatives in summarization quality (ROUGE 1: 0.60 vs. 0.52). Key innovations include language-specific stemmers for Finno-Ugric languages and cross-Uralic transfer learning, yielding 15.7% improvement in recall while maintaining 98.2% precision. Comprehensive evaluations across multiple datasets demonstrate consistent superiority over contemporary baselines, with particular emphasis on addressing Uralic language processing challenges.
%U https://aclanthology.org/2025.iwclul-1.5/
%P 29-38

Markdown (Informal)

[A Hybrid Multilingual Approach to Sentiment Analysis for Uralic and Low-Resource Languages: Combining Extractive and Abstractive Techniques](https://aclanthology.org/2025.iwclul-1.5/) (Krasitskii et al., IWCLUL 2025)

• A Hybrid Multilingual Approach to Sentiment Analysis for Uralic and Low-Resource Languages: Combining Extractive and Abstractive Techniques (Krasitskii et al., IWCLUL 2025)

ACL

• Mikhail Krasitskii, Olga Kolesnikova, Grigori Sidorov, and Alexander Gelbukh. 2025. A Hybrid Multilingual Approach to Sentiment Analysis for Uralic and Low-Resource Languages: Combining Extractive and Abstractive Techniques. In Proceedings of the 10th International Workshop on Computational Linguistics for Uralic Languages, pages 29–38, Joensuu, Finland. Association for Computational Linguistics.