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A study on the sentiments and psychology of twitter users during COVID-19 lockdown period

Authors:

Ishaani Priyadarshini,

Pinaki Mohanty,

Raghvendra Kumar,

Pradeep Kumar SinghAuthors Info & Claims

Multimedia Tools and Applications, Volume 81, Issue 19

Pages 27009 - 27031

https://doi.org/10.1007/s11042-021-11004-w

Published: 01 August 2022 Publication History

Abstract

The outbreak of the novel Coronavirus in late 2019 brought severe devastation to the world. The pandemic spread across the globe, infecting more than ten million people and disrupting several businesses. Although social distancing and the use of protective masks were suggested all over the world, the cases seem to rise, which led to worldwide lockdown in different phases. The rampant escalation in the number of cases, the global effects, and the lockdown may have a severe effect on the psychology of people. The emergency protocols implemented by the authorities also lead to increased use in the number of multimedia devices. Excessive use of such devices may also contribute to psychological disorders. Hence, hence it is necessary to analyze the state of mind of people during the lockdown. In this paper, we perform a sentiment analysis of Twitter data during the pandemic lockdown, i.e., two weeks and four weeks after the lockdown was imposed. Investigating the sentiments of people in the form of positive, negative, and neutral tweets would assist us in determining how people are dealing with the pandemic and its effects on a psychological level. Our study shows that the lockdown witnessed more number positive tweets globally on multiple datasets. This is indicative of the positivity and optimism based on the sentiments and psychology of Twitter users worldwide. The study will be effective in determining people’s mental well-being and will also be useful in devising appropriate lockdown strategies and crisis management in the future.

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Cited By

Chen JYi COkegbile SCai JShen X(2024)Networking Architecture and Key Supporting Technologies for Human Digital Twin in Personalized Healthcare: A Comprehensive SurveyIEEE Communications Surveys & Tutorials10.1109/COMST.2023.330871726:1(706-746)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/COMST.2023.3308717
Balan AQuinto ESamonte M(2023)Analysis of Sentiments and Emotions Attributes of COVID-19-related tweets in the Philippines Using time-Series AnalysisProceedings of the 7th International Conference on Education and Multimedia Technology10.1145/3625704.3625715(346-356)Online publication date: 29-Aug-2023
https://dl.acm.org/doi/10.1145/3625704.3625715
Vatsa DYadav ASingh PDiwakar M(2023)An Analytical Insight of Discussions and Sentiments of Indians on Omicron-Driven Third Wave of COVID-19SN Computer Science10.1007/s42979-023-02269-z4:6Online publication date: 14-Oct-2023
https://dl.acm.org/doi/10.1007/s42979-023-02269-z
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A study on the sentiments and psychology of twitter users during COVID-19 lockdown period

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Published In

cover image Multimedia Tools and Applications

Multimedia Tools and Applications Volume 81, Issue 19

Aug 2022

1452 pages

ISSN:1380-7501

Issue’s Table of Contents

© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021.

Publisher

Kluwer Academic Publishers

United States

Publication History

Published: 01 August 2022

Accepted: 04 May 2021

Revision received: 17 March 2021

Received: 05 July 2020

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Cited By

Chen JYi COkegbile SCai JShen X(2024)Networking Architecture and Key Supporting Technologies for Human Digital Twin in Personalized Healthcare: A Comprehensive SurveyIEEE Communications Surveys & Tutorials10.1109/COMST.2023.330871726:1(706-746)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/COMST.2023.3308717
Balan AQuinto ESamonte M(2023)Analysis of Sentiments and Emotions Attributes of COVID-19-related tweets in the Philippines Using time-Series AnalysisProceedings of the 7th International Conference on Education and Multimedia Technology10.1145/3625704.3625715(346-356)Online publication date: 29-Aug-2023
https://dl.acm.org/doi/10.1145/3625704.3625715
Vatsa DYadav ASingh PDiwakar M(2023)An Analytical Insight of Discussions and Sentiments of Indians on Omicron-Driven Third Wave of COVID-19SN Computer Science10.1007/s42979-023-02269-z4:6Online publication date: 14-Oct-2023
https://dl.acm.org/doi/10.1007/s42979-023-02269-z
Jalali MZahedi MBasiri A(2023)Deterministic solution of algebraic equations in sentiment analysisMultimedia Tools and Applications10.1007/s11042-023-15140-382:23(35457-35474)Online publication date: 29-Mar-2023
https://dl.acm.org/doi/10.1007/s11042-023-15140-3
Osop HWong JLwin SLee C(2023)Health Information Encountering: Topic Modelling and Sentiment Analysis of Pre- and Current-COVID-19 TweetsLeveraging Generative Intelligence in Digital Libraries: Towards Human-Machine Collaboration10.1007/978-981-99-8085-7_15(167-177)Online publication date: 4-Dec-2023
https://dl.acm.org/doi/10.1007/978-981-99-8085-7_15
Mehra VSingh PDwivedi YSingh GSawhney R(2022)Who’s the Bigger Brand After COVID-19 Pandemic? An Assessment of Fan Engagement During Euro 2020The Role of Digital Technologies in Shaping the Post-Pandemic World10.1007/978-3-031-15342-6_30(391-402)Online publication date: 13-Sep-2022
https://dl.acm.org/doi/10.1007/978-3-031-15342-6_30

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