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The COVID-19 pandemic brought many challenges, from hospital-occupationmanagement to lock-down mental-health repercussions such as anxiety ordepression. In this work, we present a solution for the later problem bydeveloping a Twitter emotion-monitor system based on a state-of-the-artnatural-language processing model. The system monitors six different emotionson accounts in cities, as well as politicians and health-authorities Twitteraccounts. With an anonymous use of the emotion monitor, health authorities andprivate health-insurance companies can develop strategies to tackle problemssuch as suicide and clinical depression. The model chosen for such a task is aBidirectional-Encoder Representations from Transformers (BERT) pre-trained on aSpanish corpus (BETO). The model performed well on a validation dataset. Thesystem is deployed online as part of a web application for simulation and dataanalysis of COVID-19, in Colombia, available athttps://epidemiologia-matematica.org.

COVID-19 Emotion Monitoring as a Tool to Increase Preparedness for Disease Outbreaks in Developing Regions