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

TL;DR

This paper presents a Twitter-based emotion monitoring system using BERT to track mental health-related emotions during COVID-19 in Colombia, aiding health authorities in developing targeted mental health strategies.

Contribution

It introduces a novel emotion monitoring system leveraging BERT for Spanish tweets, specifically tailored for COVID-19 mental health analysis in developing regions.

Findings

The BERT-based model achieved high accuracy on validation data.

The system effectively monitors six emotions in real-time.

Deployment demonstrates practical utility for health policy support.

Abstract

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.