Study of Text Patterns Found on Social Networks of Mental Health Reactions to COVID-19.

Abstract

Background: SARS-CoV-2 is an infectious disease caused by the coronavirus that was first reported in December 2019 in China and immediately spread around the world causing a pandemic, which has caused countless deaths and cases in global health. Mental health has not gone untouched by this pandemic; due to the lockdown and the vast amounts of information disseminated, the Panamanian population has begun to feel the collateral effects.

Objective: We propose classifying tweets using a machine learning (ML) and deep learning (DL) approach and pattern search to make recommendations to the emotional and psychological reactions of the Panamanian population.

Methods: Our study has been carried out with a corpus in spanish extracted from X for the automatic classification of texts, from which we have categorized, through the ML&DL approach, the tweets about Covid-19 in Panama, in order to know if the population has suffered any mental health effects.

Results: We can say that the ML models provide competitive results in terms of automatic identification of texts with an accuracy of 90%.

Conclusion: X is a social network and an important information channel where you can explore, analyze and organize opinions to make better decisions. Text mining and patron search are a natural language processing (NLP) task that, using ML&DL algorithms, can integrate innovative strategies into information and communication technologies.