Caracterización de riesgos urbanos en prensa aplicando minería de texto para el enriquecimiento de datos abiertos

News is freely spread and widely available to Internet users much more easily than traditional media. In the news, we can find an infinite number of hidden “minor data,” that can provide valuable information not col-lected in other sources of information. In this context, we have been interested in analyzing and characteriz-ing the urban risks contained in the Uruguayan open newspapers using text mining techniques. This pro-posal makes it possible to create a news corpus based on risk events included in open data. The corpus cov-ers 2003-2019 and is built from the digital open newspapers El Eco Digital, Montevideo Portal, and La Red 21. Various text mining techniques are applied to this corpus using the QDA-MinerLite software and the Python language (concretely, through the Scattertext library) to identify, characterize, and discover insights on these events. The corpus processing results help en-rich the existing open data on risks in Uruguay, incor-porating information on their effects, actors, and asso-ciated interventions.