Natural Language Processing is a discipline that combines computational linguistics, artificial intelligence, and machine learning to enable computers to analyse and interpret human language. It encompasses various applications such as information retrieval, machine translation, and information extraction, and can also be applied to humanities research. This paper focuses on the analysis of Neil Gaiman's novels Good Omens and American Gods using methods derived from computational and corpus linguistics. The goal of the research was to examine how adjectives are used to describe characters and how this reflects broader themes and messages within these works. The research was divided into three phases. In the first phase, a corpus was collected and cleaned using Calibre and Notepad++ programs. The second phase involved creating dictionaries and syntactic grammars to identify adjectives and the characters they describe in the NooJ program. The third phase addressed the problem of coreference resolution through the application of machine learning in the Python programming language. The results indicate differences in the way characters are described between the two novels. In American Gods, there is a dominance of detailed characterization of the main characters through adjectives, while in Good Omens, a humorous writing style is used that does not rely on describing characters through adjectives. These insights point to different approaches in literary analysis of characterization, from detailed descriptions in American Gods to creative stylistic figures in Good Omens.