Synthesis of classical and digital approaches to the study of special lexis in the fashion & clothing fields

The relevance of the study stems from the dynamic transformation of the “Fashion” and “Clothing” special lexical systems under globalization and digitalization, which requires a synthesis of qualitative analysis of cultural nuances and quantitative processing of big data. Traditional linguistic methods, such as componential and comparative analysis, have limited scalability, while digital technologies (NLP, graph models) offer new opportunities for studying semantic shifts and cross-cultural borrowings. The aim of the paper is to systematize methods for analyzing the special lexical system through the integration of classical approaches with NLP algorithms, large language models (BERT), and graph-based methods. Materials and methods included latent semantic analysis (LSA) to identify thematic patterns in a corpus of historical and modern texts, BERT neural network models for analyzing polysemy and diachronic shifts of special lexemes, and graph models to visualize connections in the ASOS dataset. Results revealed that LSA uncovered oppositions such as “historical vs. contemporary lexicon” and “exclusivity vs. functionality”, confirming the cultural peculiarities of special lexis. The BERT model demonstrated the dynamics of meaning, in particular, the evolution of the special lexeme denim from workwear to sustainable fashion. Graph models visualized attribute clusters (colour, fabric) and bridge nodes, such as the special lexeme drape, reflecting structural connections within the special lexical system. The conclusion emphasizes that the combined approach enhances scalability and contextual sensitivity of analysis, opening prospects for trend forecasting and cross-cultural studies. Limitations relate to the need for verifying automated methods through qualitative analysis, while future directions include integrating temporal parameters into graph models and applying multilingual LLMs.