Text Mining for Big Linguistic Data in Modern Philological Conditions: Dynamic Conditions of Uncertainty, Turbulence and Risks

Abstract

Introduction. The modern development of digital humanities is accompanied by an unprecedented growth in the volume of text information generated in the scientific and educational [1], social, political, and media spheres (billions of text documents are created every day, presented in different languages, genres, and formats [2]). Such dynamics lead to the formation of Big Linguistic Data arrays, the traditional methods of processing, analysis, and analytics of which are not sufficiently effective [3]. At the same time, the modern global environment is characterized by a high degree of uncertainty, turbulence, and systemic risks, which is described by modern concepts of VUCA, BANI, SHIVA, and WARMI - in these conditions, the requirements for speed, objectivity, and scalability of text information analysis methods are significantly increasing [4]. Philology is gradually transforming from a predominantly descriptive discipline into a field that actively uses methods of intelligent data analysis, artificial intelligence, and computational linguistics [5], [6]. A special place among these technologies is occupied by Text Mining - a set of methods for automatically extracting knowledge from large collections of unstructured texts [7]; unlike classical content analysis, Text Mining technology allows you to identify hidden patterns [8], semantic relationships, thematic structures, emotional characteristics of texts and the dynamics of changes in language processes based on the analysis of millions of documents [9]. The relevance of the study is determined by the need to develop modern approaches to processing large linguistic corpora in the conditions of constant growth in information flows, the complexity of the communication environment and the acceleration of the digital transformation of the humanities. The aim of the article is to study the capabilities of Text Mining technology during the analysis of large linguistic data, determine the main areas of its application in modern philology, as well as assess the prospects for using methods of intellectual text analysis in conditions of high uncertainty and systemic instability.