情绪分析
人气
2019年冠状病毒病(COVID-19)
舆论
主题模型
网络爬虫
社会化媒体
微博
潜在Dirichlet分配
数据科学
计算机科学
互联网隐私
万维网
政治学
情报检索
人工智能
政治
医学
疾病
病理
法学
传染病(医学专业)
作者
Runbin Xie,Samuel Kai Wah Chu,Dickson K.W. Chiu,Yangshu Wang
出处
期刊:Data and Information Management
[De Gruyter]
日期:2021-01-01
卷期号:5 (1): 86-99
被引量:23
标识
DOI:10.2478/dim-2020-0023
摘要
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.
科研通智能强力驱动
Strongly Powered by AbleSci AI