TY - JOUR
T1 - Exploring Public Response to COVID-19 on Weibo with LDA Topic Modeling and Sentiment Analysis
AU - Xie, Runbin
AU - Chu, Samuel Kai Wah
AU - Chiu, Dickson Kak Wah
AU - Wang, Yangshu
N1 - Publisher Copyright:
© 2021 Runbin Xie et al., published by Sciendo
PY - 2021/1/1
Y1 - 2021/1/1
N2 - It is necessary and important to understand public responses to crises, including disease outbreaks. Traditionally, surveys have played an essential role in collecting public opinion, while nowadays, with the increasing popularity of social media, mining social media data serves as another popular tool in opinion mining research. To understand the public response to COVID-19 on Weibo, this research collects 719,570 Weibo posts through a web crawler and analyzes the data with text mining techniques, including Latent Dirichlet Allocation (LDA) topic modeling and sentiment analysis. It is found that, in response to the COVID-19 outbreak, people learn about COVID-19, show their support for frontline warriors, encourage each other spiritually, and, in terms of taking preventive measures, express concerns about economic and life restoration, and so on. Analysis of sentiments and semantic networks further reveals that country media, as well as influential individuals and “self-media,” together contribute to the information spread of positive sentiment.
AB - It is necessary and important to understand public responses to crises, including disease outbreaks. Traditionally, surveys have played an essential role in collecting public opinion, while nowadays, with the increasing popularity of social media, mining social media data serves as another popular tool in opinion mining research. To understand the public response to COVID-19 on Weibo, this research collects 719,570 Weibo posts through a web crawler and analyzes the data with text mining techniques, including Latent Dirichlet Allocation (LDA) topic modeling and sentiment analysis. It is found that, in response to the COVID-19 outbreak, people learn about COVID-19, show their support for frontline warriors, encourage each other spiritually, and, in terms of taking preventive measures, express concerns about economic and life restoration, and so on. Analysis of sentiments and semantic networks further reveals that country media, as well as influential individuals and “self-media,” together contribute to the information spread of positive sentiment.
KW - COVID-19
KW - LDA
KW - Weibo
KW - sentiment analysis
KW - web crawling
UR - http://www.scopus.com/inward/record.url?scp=85103754747&partnerID=8YFLogxK
U2 - 10.2478/dim-2020-0023
DO - 10.2478/dim-2020-0023
M3 - Article
AN - SCOPUS:85103754747
VL - 5
SP - 86
EP - 99
JO - Data and Information Management
JF - Data and Information Management
IS - 1
ER -