Adjective as a Means of Expressing the Category of Evaluation in Ukrainian and English

This article presents a comprehensive analysis of non-standard forms of comparative and superlative adjectives in Ukrainian and English, with a focus on their evaluative function and deviations from prescriptive grammatical models. Within the framework of traditional grammar, the formation of comparative degrees is strictly regulated; however, contemporary language practice demonstrates an increasing tendency towards the use of structurally unconventional comparative forms. These forms are most commonly found in advertising, informal speech and literary discourse, where grammatical deviations are often justified by communicative intentions, such as attracting attention, creating expressive effect or enhancing stylistic distinctiveness. The study adopts a corpusdriven approach, which enables the identification of usage patterns in authentic texts and facilitates a quantitative and qualitative examination of their evaluative potential. It has been established that positively marked constructions predominate in both languages (English — 78.5%, Ukrainian — 80.2%), with the highest frequency observed in advertising discourse, where the purpose is to construct a favourable image of a product or service. At the same time, significant cross-linguistic differences have been revealed: English exhibits a greater degree of grammatical flexibility and tolerance for norm violations, whereas Ukrainian maintains a more conservative approach, allowing such constructions primarily in expressive or commercial contexts. The study also highlights the range of pragmatic and stylistic functions performed by non-standard comparatives, including hyperbolic emphasis, rhetorical intensification, stylistic enhancement and brand identity construction. The findings are relevant for the development of contrastive grammar and the grammar of evaluation, as well as for applied branches of linguistics, particularly computational linguistics and natural language processing. The insights gained may be applied in the design of algorithms for automatic detection and interpretation of evaluative expressions, especially in sentiment analysis, advertising content creation, human–machine interaction interfaces and other systems involving the processing of informal language.