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This study presents an innovative text mining methodology that supports policy analysts with problem recognition, definition and selection. The empirical analysis is based on four years of online news articles published in the period 2016–2019. Using a combination of text mining methods (analysis of term-frequencies, co-occurrence and sentiment analysis), we identify trending terms and explore selected regulatory issues. The analysis demonstrates that while each text mining algorithm provides insightful results, their combination yields more detailed and robust overview of regulatory problems. The results present early signals and trends, the connections between trending topics, and the changing public attitudes towards them.