  The COVID-19 pandemic brought many challenges, from hospital-occupation
management to lock-down mental-health repercussions such as anxiety or
depression. In this work, we present a solution for the later problem by
developing a Twitter emotion-monitor system based on a state-of-the-art
natural-language processing model. The system monitors six different emotions
on accounts in cities, as well as politicians and health-authorities Twitter
accounts. With an anonymous use of the emotion monitor, health authorities and
private health-insurance companies can develop strategies to tackle problems
such as suicide and clinical depression. The model chosen for such a task is a
Bidirectional-Encoder Representations from Transformers (BERT) pre-trained on a
Spanish corpus (BETO). The model performed well on a validation dataset. The
system is deployed online as part of a web application for simulation and data
analysis of COVID-19, in Colombia, available at
https://epidemiologia-matematica.org.
