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 < 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.

This study examines sentiment analysis in Tamil-English code-mixed texts using advanced transformer-based architectures. The unique linguistic challenges, including mixed grammar, orthographic variability, and phonetic inconsistencies, are addressed. Data limitations and annotation gaps are discussed, highlighting the need for larger datasets. The performance of models such as XLM-RoBERTa, mT5, IndicBERT, and RemBERT is evaluated, with insights into their optimization for low-resource, code-mixed environments.

This article is dedicated to the study of multilingual approaches to sentiment analysis of texts in Finnish, Hungarian, and Bulgarian. For Finnish and Hungarian, which are characterized by complex morphology and agglutinative grammar, an analysis was conducted using both traditional rule-based methods and modern machine learning techniques. In the study, BERT, XLM-R, and mBERT models were used for sentiment analysis, demonstrating high accuracy in sentiment classification. The inclusion of Bulgarian was motivated by the opportunity to compare results across languages with varying degrees of morphological complexity, which allowed for a better understanding of how these models can adapt to different linguistic structures. Datasets such as the Hungarian Emotion Corpus, FinnSentiment, and SentiFi were used to evaluate model performance. The results showed that transformer-based models, particularly BERT, XLM-R, and mBERT, significantly outperformed traditional methods, achieving high accuracy in sentiment classification tasks for all the languages studied.