Semantic search for public opinions on urban affairs: A probabilistic topic modeling-based approach
作者：
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.
摘要
•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.
论文关键词：Probabilistic topic modeling,Public opinions,Big data analysis,Semantic search,Latent Dirichlet allocation (LDA)
论文评审过程：Received 26 December 2014, Revised 12 September 2015, Accepted 14 October 2015, Available online 14 November 2015, Version of Record 9 March 2016.
论文官网地址：https://doi.org/10.1016/j.ipm.2015.10.004
