Analysis and visualization of COVID-19 discourse on Twitter using data science: a case study of the USA, the UK and India
Analysis and visualization of COVID-19 discourse on Twitter using data science: a case study of the USA, the UK and India
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使用数据科学对 Twitter 上的 COVID-19 话语进行分析和可视化：美国、英国和印度的案例研究
DOI：
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发表时间：
2021
期刊：
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通讯作者：
Deniz Appelbaum
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作者：
Haider Ilyas;A. Anwar;Ussama Yaqub;Zamil S. Alzamil;Deniz Appelbaum
目的：本文旨在使用数据科学措施理解，检查和解释英国，美国和印度人们对COVID-19大流行的主要关注和情绪。设计/方法/方法：本研究采用无监督和有监督的机器学习方法，即对Twitter数据进行主题建模和情感分析，提取讨论主题并计算公众情绪。调查结果：在大流行的头三个月，政府和政策制定者仍然是Twitter上公众讨论的焦点。总体而言，除美国外，公众对疫情的态度保持中性。原创性/价值：本文提出了一种基于数据科学的方法，以更好地理解COVID-19大流行期间关注的公共话题。©2021，翡翠出版有限公司
Purpose: This paper aims to understand, examine and interpret the main concerns and emotions of the people regarding COVID-19 pandemic in the UK, the USA and India using Data Science measures. Design/methodology/approach: This study implements unsupervised and supervised machine learning methods, i.e. topic modeling and sentiment analysis on Twitter data for extracting the topics of discussion and calculating public sentiment. Findings: Governments and policymakers remained the focus of public discussion on Twitter during the first three months of the pandemic. Overall, public sentiment toward the pandemic remained neutral except for the USA. Originality/value: This paper proposes a Data Science-based approach to better understand the public topics of concern during the COVID-19 pandemic. © 2021, Emerald Publishing Limited.