Analysis and visualization of COVID-19 discourse on Twitter using data science: a case study of the USA, the UK and India

被引：5

作者：

Ilyas, Haider ^{[1
]}

Anwar, Ahmed ^{[1
]}

Yaqub, Ussama ^{[1
]}

Alzamil, Zamil ^{[2
]}

Appelbaum, Deniz ^{[3
]}

机构：

[1] Lahore Univ Management Sci, Lahore, Pakistan

[2] Majmaah Univ, Al Majmaah, Saudi Arabia

[3] Montclair State Univ, Montclair, NJ USA

来源：

GLOBAL KNOWLEDGE MEMORY AND COMMUNICATION | 2022年 / 71卷 / 03期

关键词：

Topic modeling; Sentiment analysis; COVID-19; Twitter; Data science; Machine learning; INFORMATION; TWEETS;

D O I：

10.1108/GKMC-01-2021-0006

中图分类号：

G25 [图书馆学、图书馆事业]; G35 [情报学、情报工作];

学科分类号：

1205 ; 120501 ;

摘要：

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.

引用

页码：140 / 154

页数：15

共 48 条

[1] Top Concerns of Tweeters During the COVID-19 Pandemic: Infoveillance Study [J].