Semantic search for public opinions on urban affairs: A probabilistic topic modeling-based approach. Issue 3 (May 2016) - Ma, Baojun; Zhang, Nan; Liu, Guannan; Li, Liangqiang; Yuan, Hua
Highlights: The explosion of online user-generated content (UGC) and the development of big data analysis provide a new opportunity and challenge to understand and respond to public opinions in the G2C e-government context. We proposed an approach based on the latent Dirichlet allocation (LDA) and designed a practical system to provide users with satisfying searching results and the longitudinal changing curves of related topics. Municipal administrators could better understand citizens' online comments based on the proposed semantic search approach and could improve their decision-making process by considering public opinions. Abstract: The explosion of online user-generated content (UGC) and the development of big data analysis provide a new opportunity and challenge to understand and respond to public opinions in the G2C e-government context. To better understand semantic searching of public comments on an online platform for citizens' opinions about urban affairs issues, this paper proposed an approach based on the latent Dirichlet allocation (LDA), a probabilistic topic modeling method, and designed a practical system to provide users—municipal administrators of B-city—with satisfying searching results and the longitudinal changing curves of related topics. The system is developed to respond to actual demand from B-city's local government, and the user evaluation experiment results show that a system based on the LDA method could provide information that is more helpful to relevant staff members. Municipal administrators could better understand citizens' online comments based on the proposed semantic search approach and could improve their decision-making process by considering public opinions.