This study introduces an approach for measuring sentiment and uncertainty indices in Colombia through text mining. Economic news from digital media, spanning March 2020 to September 2024, is analyzed using dictionary-based methods and predefined word lists. The constructed indices reflect major macroeconomic events, such as the phased reopening during the pandemic, the national strike in May 2021, and the decline in demand associated with elevated inflation. These indices function as leading indicators and exhibit statistically significant associations with high-frequency economic data. Incorporating news-based sentiment and uncertainty indices improves the precision of nowcasting Colombia’s economic activity using a dynamic factor model. The results indicate that incorporating qualitative, forward-looking news with traditional data enhances the monitoring of short-term economic fluctuations and the identification of turning points.