Monitoring the Emotional Response to the COVID-19 Pandemic Using Sentiment Analysis: A Case Study in Mexico

Abstract

The world is facing the COVID-19 pandemic, leading to an unprecedented change in the lifestyle routines of millions. Beyond the general physical health, financial, and social repercussions of the pandemic, the adopted mitigation measures also present significant challenges in the population’s mental health and health programs. It is complex for public organizations to measure the population’s mental health in order to incorporate it into their own decision-making process. Traditional survey methods are time-consuming, expensive, and fail to provide the continuous information needed to respond to the rapidly evolving effects of governmental policies on the population’s mental health. A significant portion of the population has turned to social media to express the details of their daily life, rendering this public data a rich field for understanding emotional and mental well-being. This study aims to track and measure the sentiment changes of the Mexican population in response to the COVID-19 pandemic. To this end, we analyzed 760,064,879 public domain tweets collected from a public access repository to examine the collective shifts in the general mood about the pandemic evolution, news cycles, and governmental policies using open sentiment analysis tools. Sentiment analysis polarity scores, which oscillate around -0.15, show a weekly seasonality according to Twitter’s usage and a consistently negative outlook from the population. It also remarks on the increased controversy after the governmental decision to terminate the lockdown and the celebrated holidays, which encouraged the people to incur social gatherings. These findings expose the adverse emotional effects of the ongoing pandemic while showing an increase in social media usage rates of 2.38 times, which users employ as a coping mechanism to mitigate the feelings of isolation related to long-term social distancing. The findings have important implications in the mental health infrastructure for ongoing mitigation efforts and feedback on the perception of policies and other measures. The overall trend of the sentiment polarity is 0.0001110643.

Using Big Data and Serverless Architecture to Follow the Emotional Response to the COVID-19 Pandemic in Mexico

“…For the preprocessing performed on the text, we follow the standard practices common in natural language processing projects: removing punctuations and emojis, URLs, stop words, converting the text to lower casing, tokenization, stemming, and lemmatization. We then perform sentiment analysis and a time series analysis on the remaining tweets to do the actual sentiment polarity tracking, better described by [16].…”

“…Next, a quick summary of the methodology followed by [16] is presented. Natural language processing has several architectures available to implement language models, each with their differences in robustness and accuracy for several tasks, such as sentiment polarity determination.…”

“…These were filtered down to n = 2, 142, 800 unique tweets and stripped of any metadata, with light text preprocessing to clean URLs and similar operations. The sentiment polarity was then calculated using several language models, from which a time series was generated, followed by a similar analysis to that of [16].…”

“…Thus, it is essential to have a system independent of the language model selected for the task, making it simple to choose and change the language model if needed. This work describes the architecture developed to perform this sentiment analysis study, already reported by [16], going in-depth on the design, advantages, and disadvantages of utilizing big data in serverless cloud-based architectures for a project of this size. Next, we present a brief exploration of related works, followed by the methodology followed in the study, the architecture implemented as well as infrastructure-related information, the experiments performed, and closing with a description of the results and a brief discussion of those results.…”

Dynamic Evaluation of Transformation Ability for Emergency Scientific Research Achievements Based on an Improved Minimum Distance-Maximum Entropy Combination Weighting Method: A Case Study of COVID-19 Epidemic Data

“…It has resulted in the death of hundreds of thousands of people around the world, posed an urgent challenge to medical professionals, and exposed the weaknesses of national health systems [2,3]. COVID-19 rapidly caused major economic and social disruptions on an unprecedented scale [4,5].…”