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This article presents a study aimed at developing an optimal concept for analyzing and comparing information sources based on large amounts of text information using natural language processing (NLP) methods. The object of the study was Telegram news channels, which are used as sources of text data. Pre-processing of texts was carried out, including cleaning, tokenization and lemmatization, to form a global dictionary consisting of unique words from all information sources.

Examined the medical term in the aspect of modern text theory. Described its potential capabilities in expressing the text category “Informativness”, which creates the national language picture of the world and reflects the specifics of the scientific thinking of Ukrainians.

The subject of this research is epistemic modality in the scientific and popular discourse as the part of speech information and evaluation of teachers or students that form the structure of the language in different languages, using various means of expression, confidence, doubt and certainty of the truth of incomplete information. These means are determinative or cognitive markers of the subject of speech – teacher or student.

The scientific and practical problem of automatic detection of meaningful keywords and Ukrainian content categorization in Internet systems on the basis of linguistic analysis of text information is unleashed. The article presents a theoretical and experimental substantiation of linguistic analysis methods for Ukrainian content using Porter stemming.