Abstract
Monitoring public attitudes towards corruption is critical for governments, organisations and researchers seeking to understand the causes of its spread and ways to prevent and counteract these destructive processes in society. The article is devoted to analysing the corruption-related tone of media publications and making an analysis of the content on corruption in the world and in Ukraine. The relevance of solving this scientific problem is due to the fact that the use of machine learning methods allows processing large amounts of data from various sources and obtaining a detailed and dynamic understanding of public attitudes towards corruption. The article analyses the areas of application of sentiment analysis at different levels of economic relations. Machine learning methods, namely the VADER analyser, were used to study the tone of the text of publications about corruption. The articles published in The Guardian were selected for analysis. The study period was 2021– 2024. For the analysis of the text tone, Orange Data Mining was chosen. The study of the issue of tone of texts in the media in the article is carried out in the following logical sequence: collection of text data on corruption, data pre-processing (cleaning and preparing the text for classification), transformation of textual data into numerical representations for machine learning models, and sentiment classification based on machine learning methods. The study found that during the first six months of the year with about 910 publications on corruption issues were published annually. One third of the corruption stories in The Guardian in 2022 mentioned Ukraine. The analysis showed that the dominant emotional tone of articles about corruption in the world was negative. At the same time, the emotional colouring of news texts on corruption in Ukraine is more negative compared to world news on similar issues. The results of the study are of practical importance and can be used by non-governmental organizations, government institutions and international organisations in assessing the effectiveness of policies to prevent and combat corruption in the country.
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