The term “tonality”: an integrative approach in linguistic research

The present study aims to systematically refine the term “tonality” within the linguistic paradigm through an analysis of related categories and a synthesis of interdisciplinary approaches to its interpretation. The relevance of the research is driven by terminological dissimilarity and the lack of consensus in defining textual tonality, which complicates its application in both theoretical and applied studies. The article’s objective is to develop a comprehensive approach to tonality as a category, integrating existing interpretations from adjacent fields of linguistics: discourse analysis, stylistics, emotion linguistics, and computational linguistics. The methodology is grounded in an interdisciplinary analysis of works addressing the phenomenon of tonality, with an emphasis on identifying overlaps in their interpretations. The study systematizes approaches to classifying tonality types and clarifies its interrelation with categories such as discourse, modality, emotiveness, and evaluation. The scientific novelty lies in integrating heterogeneous concepts into a unified model that addresses the fragmented understanding of tonality. A key outcome of the research is the formulation of a working definition of tonality as a multidimensional textual category expressing the author’s emotional-evaluative stance through linguistic markers, contextual factors, and pragmatic intentions. Based on a comparative analysis, a classification of tonality types is proposed, accounting for their functional specificity. The practical significance of the study lies in the potential application of the developed model in applied fields such as automated tonality analysis (SA), media content analysis, stylistic examination, and the interpretation of discursive strategies. The unification of terminology and the structuring of tonality identification criteria may enhance the accuracy of linguistic and computational text-processing methods. The findings open avenues for further research on tonality within cognitive-discursive frameworks, as well as for the development of machine learning algorithms tailored to the multicomponent nature of this category

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