Semantic search for public opinions on urban affairs: A probabilistic topic modeling-based approach

被引:42
作者
Ma, Baojun [1 ]
Zhang, Nan [2 ]
Liu, Guannan [3 ]
Li, Liangqiang [4 ]
Yuan, Hua [4 ]
机构
[1] Beijing Univ Posts & Telecommun, Sch Econ & Management, Beijing 100876, Peoples R China
[2] Tsinghua Univ, Sch Publ Policy & Management, Beijing 100084, Peoples R China
[3] Tsinghua Univ, Sch Econ & Management, Beijing 100084, Peoples R China
[4] Univ Elect Sci & Technol China, Sch Management & Econ, Chengdu 611731, Peoples R China
基金
中国国家自然科学基金;
关键词
Probabilistic topic modeling; Public opinions; Big data analysis; Semantic search; Latent Dirichlet allocation (LDA); FRAUD DETECTION; ONTOLOGY; WEB; RETRIEVAL; GOVERNMENT; IDENTIFICATION; FRAMEWORK; RANKING;
D O I
10.1016/j.ipm.2015.10.004
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
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. 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. (C) 2015 Elsevier Ltd. All rights reserved.
引用
收藏
页码:430 / 445
页数:16
相关论文
共 85 条
[1]  
Allan J., 2002, Proceedings of SIGIR 2002. Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, P307
[2]  
An XD, 2013, SIGIR'13: THE PROCEEDINGS OF THE 36TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH & DEVELOPMENT IN INFORMATION RETRIEVAL, P829
[3]  
[Anonymous], P SEM KNOWL MAN SEM
[4]  
Balakrishnan N., 2005, PRIMER STAT DISTRIBU
[5]   Investigating Topic Models' Capabilities in Expression Microarray Data Classification [J].
Bicego, Manuele ;
Lovato, Pietro ;
Perina, Alessandro ;
Fasoli, Marianna ;
Delledonne, Massimo ;
Pezzotti, Mario ;
Polverari, Annalisa ;
Murino, Vittorio .
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS, 2012, 9 (06) :1831-1836
[6]   A3CRank: An adaptive ranking method based on connectivity, content and click-through data [J].
Bidoki, Ali Mohammad Zareh ;
Ghodsnia, Pedram ;
Yazdani, Nasser ;
Oroumchian, Farhad .
INFORMATION PROCESSING & MANAGEMENT, 2010, 46 (02) :159-169
[7]   Repeatable and reliable semantic search evaluation [J].
Blanco, Roi ;
Halpin, Harry ;
Herzig, Daniel M. ;
Mika, Peter ;
Pound, Jeffrey ;
Thompson, Henry S. ;
Thanh Tran .
JOURNAL OF WEB SEMANTICS, 2013, 21 :14-29
[8]  
Blei D.M., 2006, INT C MACHINE LEARNI, DOI DOI 10.1145/1143844.1143859
[9]   Probabilistic Topic Models [J].
Blei, David M. .
COMMUNICATIONS OF THE ACM, 2012, 55 (04) :77-84
[10]   Latent Dirichlet allocation [J].
Blei, DM ;
Ng, AY ;
Jordan, MI .
JOURNAL OF MACHINE LEARNING RESEARCH, 2003, 3 (4-5) :993-1022