Purpose
The suddenness, urgency and social publicity of emergency events lead to great impacts on public life. The deep analysis of emergency events can provide detailed and comprehensive information for the public to get trends of events timely. With the development of social media, users prefer to express opinions on emergency events online. Thus, massive public opinion information of emergencies has been generated. Hence, this paper aims to conduct multidimensional mining on emergency events based on user-generated contents, so as to obtain finer-grained results.
Design/methodology/approach
This paper conducted public opinion analysis via fine-grained mining. Specifically, public opinion about an emergency event was collected as experimental data. Secondly, opinion mining was conducted to get users’ opinion polarities. Meanwhile, users’ information was analysed to identify impacts of users’ characteristics on public opinion.
Findings
The experimental results indicate that public opinion is mainly negative in emergencies. Meanwhile, users in developed regions are more active in expressing opinions. In addition, male users, especially male users with high influence, are more rational in public opinion expression.
Originality/value
To the best of the authors’ knowledge, this is the first research to identify public opinion in emergency events from multiple dimensions, which can get in-detail differences of users’ online expression.
Voice based Public Opinion Mining and Automatic Category System