APPLICATION OF NATURAL LANGUAGE PROCESSING TO IDENTIFY EXTREME HYDROMETEOROLOGICAL EVENTS IN DIGITAL NEWS MEDIA: CASE OF THE MAGDALENA RIVER BASIN, COLOMBIA

Abstract

Human behavior and decision making are dynamically influenced by digital media, making digital news a vast data source of events and points of view. Meanwhile, artificial intelligence now has advanced capacity for natural language processing (NLP) through tools such as sentiment analysis and topic identification. This research uses machine learning algorithms to prove the hypothesis that it is possible to find correlations between news information and water resource problems. The 207 water bodies in the Magdalena River Basin, Colombia, were analyzed alongside 19,490 news articles published between 2016 and 2020 in 42 online newspapers. A platform for the visualization of spatiotemporal information was developed as a proof of concept. There has been a noticeable increase in digital news about the Magdalena River Basin in recent years and some correlation with extreme events, but the comparison between the sentiments and measured physical variables was inconclusive. This is likely due to the difficulty in filtering the topic from obtained news, as well as in precisely identifying the spatial location and temporal range of events. In the future, NLP techniques to analyze news articles could be used to provide additional information for decision making at the basin level.