Exploring Occupation Differences in Reactions to COVID-19 Pandemic on Twitter

被引：1

作者：

Zhao Y. ^{[1
]}

Xi H. ^{[1
]}

Zhang C. ^{[1
]}

机构：

[1] Department of Information Management, School of Economics and Management, Nanjing University of Science and Technology, Nanjing

来源：

Data and Information Management | 2021年 / 5卷 / 01期

关键词：

COVID-19; occupational differences; sentiment analysis; topic discovery; Twitter;

D O I：

10.2478/dim-2020-0032

中图分类号：

学科分类号：

摘要：

Coronavirus disease 2019 (COVID-19) pandemic-related information are flooded on social media, and analyzing this information from an occupational perspective can help us to understand the social implications of this unprecedented disruption. In this study, using a COVID-19-related dataset collected with the Twitter IDs, we conduct topic and sentiment analysis from the perspective of occupation, by leveraging Latent Dirichlet Allocation (LDA) topic modeling and Valence Aware Dictionary and sEntiment Reasoning (VADER) model, respectively. The experimental results indicate that there are significant topic preference differences between Twitter users with different occupations. However, occupation-linked affective differences are only partly demonstrated in our study; Twitter users with different income levels have nothing to do with sentiment expression on covid-19-related topics. © 2021 Yi Zhao et al., published by Sciendo

引用

页码：110 / 118

页数：8

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