Emotion Detection Based on Bangladeshi People’s Social Media Response on COVID-19

Abstract

The COVID-19 pandemic is still active on a global scale while the virus was first identified in December 2019 in Wuhan, China. As the pandemic continues to affect millions of lives, several countries including Bangladesh have gone into complete lockdown for a second time. During the lockdown periods, people have expressed their experiences, curiosities, and ideas regarding the problems caused by the pandemic in terms of health and socioeconomic issues. This study was conducted to determine how Bangladeshi people are responding to and dealing with the circumstances of COVID-19. This study took into account the status and comments on those issues related to COVID-19 from a variety of Facebook pages and YouTube channels run by reputable Bangladeshi news organizations and health experts. Throughout the study, several machine learning methods were studied, ranging from classical algorithms which include SVM and Random Forest, while CNN and LSTM are the deep learning algorithms to conduct experiments on a classified data set that belongs to the authors, which contains 10,581 data points. While evaluating the efficiency of these models in terms of model assessment, the finding suggests that LSTM outperforms all others with an accuracy of 84.92.