An X Study of the Evolution of COVID-19-Related Sentiments in the UK

Abstract

An outbreak of SARS-CoV-2 caused the World Health Organisation (WHO) to declare a public health emergency of international concern on 30 January 2020. As the emergency escalated, the WHO declared it a global pandemic on 11 March 2020, triggering a parallel outbreak of fear and depression throughout the world, which negatively impacted the wellbeing of the public and healthcare workers alike. While helping to accelerate mental health diagnoses, we explored the use of sentiment analysis, a powerful tool for understanding opinions. We developed a machine learning classifier to detect depression, a common COVID-19-related mood disorder. To examine the shifting emotional landscape of the public discourse surrounding COVID-19, we studied two X—formerly known as Twitter—collections: one from 2020 and another one from 2022. We complemented our work with the utilisation of an off-the-shelf classifier and concluded that, over a span of two years—between 2020 and 2022—fear was the most dominant emotion attached to COVID-19 and depression the most dominant mood. Our practical insights can help to design strategic choices concerning the wellbeing of people worldwide.

Rights

https://creativecommons.org/licenses/by/4.0/

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Identifiers

DOI

http://dx.doi.org/10.5772/intechopen.1008866

Repository URI

https://hdl.handle.net/2164/25043