Detecting Emotional Evolution on Twitter during the COVID-19 Pandemic Using Text Analysis.

Javier Cabezas,Isaac Martín De Diego,Alberto Fernández-Isabel,Daniela Moctezuma

doi:10.3390/ijerph18136981

Abstract

Early in 2020, an unexpected and hazardous situation occurred threatening and challenging all of humankind. A new coronavirus called SARS-CoV-2 was first identified in Wuhan, China, and its related disease, called COVID-19, has induced one of the most dangerous crises at a global level since World War II. The ultra-fast transmission rate of the virus and the high mortality rate led the World Health Organization (WHO) to officially declare the situation a pandemic. Governments, for their part, were forced to implement unprecedented mobility restrictions and cease a large part of their economic activities. These facts triggered multiple reactions from people who expressed their feelings mainly through social networks (like Twitter), using them as vectors of information and opinion. In this paper, a study carried out in different Spanish speaking countries (Chile, Mexico, Peru, and Spain) is presented, which addresses the manner in which the evolution of the pandemic outbreak has affected the emotions expressed by individuals on Twitter over the last 13 months (from March 2020 to March 2021). We used a total of 3 million tweets to achieve this task. We made use of a well-known framework called EmoWeb to capture the dynamic variation in the sentimental value of pandemic-related words. The results reflect to what degree the pandemic and its derived problems have influenced and affected the population of the selected countries in different ways. The outcomes also illustrate the evolution over time of opinions published on Twitter regarding several topics related to COVID-19.

Highlights

Human beings have been historically defined by their ability to feel emotions [1]
For topic 1, we focused on collecting opinions on the measures and corrective actions imposed by the governments of the selected countries
The Visualisation Module produces relationships between lexicon words through 5D word clouds and word–sentiment fluctuation graphs over time. These visualisations conform to the main focus of the present paper where EmoWeb is exposed to tweets related to the COVID-19 pandemic and a graphical analysis is performed to establish a comparison between the different countries of interest

Summary

Introduction

Human beings have been historically defined by their ability to feel emotions [1]. These different emotions affect their behaviour and decision-making processes. Delving into the behaviour of human beings, they can be classified as gregarious animals by definition [3], and they usually need social interactions with other members in the community in order to maintain their emotional stability [4]. This fact has made them prone to express emotions and communicate them to other individuals since prehistoric times. It includes some details about the trends and the state of the art in the domain of application. Though the mortality rate of the virus is significantly low, millions of deaths have been registered all over the world

Methods

Results

Conclusion