The application of generative models ChatGPT and DeepSeek in the qualitative and quantitative analysis of media texts

The subject of the research is the capabilities and limitations of modern generative language models ChatGPT and DeepSeek in conducting a comprehensive analysis of media texts. The work examines their ability to process qualitative and quantitative characteristics of news materials, including lexical-statistical parameters (word frequency, unique vocabulary), syntactic features (sentence complexity, use of grammatical constructions), and stylistic markers (emotional coloring, clichéd expressions). Special attention is paid to the models' ability to identify manipulative techniques and potentially misleading information in media discourse. The main goal of the research is to conduct a comparative assessment of the effectiveness of generative models in analyzing media content of various themes and volumes. The study aims to determine the degree of reliability of automated analysis, the models' ability to identify key topics and their sentiment, as well as their potential to prevent the spread of fake information. The research utilized a sample of 10 texts from RIA Novosti, differing in theme and volume. The analysis was conducted in three directions: lexical-statistical, syntactic, and stylistic. Manual analysis was used for result verification. The methodology included a standardized prompt for both models, which ensured uniform evaluation criteria. The main findings of the conducted research are as follows. Firstly, the fundamental possibility of using generative language models for comprehensive analysis of media texts has been proven, including the identification of stylistic features, key topics, and sentiment of the materials. Secondly, it has been established that modern models demonstrate high effectiveness in qualitative analysis, comparable to expert evaluation, but have significant limitations in the accuracy of quantitative measurements of textual parameters. Thirdly, the models show the ability to recognize manipulative language techniques and potentially unreliable information. A significant contribution to the research topic is the development of a standardized methodology for applying generative models for media content analysis, which includes a comprehensive three-component approach (lexical-statistical, syntactic, and stylistic analysis) and criteria for assessing the reliability and objectivity of media texts. The results obtained are of great importance for the development of digital media linguistics and the improvement of editorial processes. Promising directions for further research include enhancing the accuracy of quantitative analysis, adapting the methodology for multilingual texts, and integrating with other AI fact-checking technologies.