Semantic Fusion and Propagation Model for Internet Public Opinion Data in Big Data Environment

doi:10.23940/ijpe.19.12.p1.30993107

Int J Performability Eng ›› 2019, Vol. 15 ›› Issue (12): 3099-3107.doi: 10.23940/ijpe.19.12.p1.30993107

Semantic Fusion and Propagation Model for Internet Public Opinion Data in Big Data Environment

Pengju Wang^a,*, Huifeng Xue^a, Zhe Yu^b, and Feng Zhang^c

^aSchool of Automation, Northwestern Polytechnical University, Xi'an, 710072, China;
^bInformation Comprehensive Office, Yan'an Municipal Committee, Yan'an, 719000, China;
^cSchool of Information Engineering, Yulin University, Yulin, 719000, China

Submitted on ; Revised on ; Accepted on
Contact: * E-mail address: 416848742@qq.com
About author:Pengju Wang is received the MS degree in Computer science from Northwestern Polytechnical University in 2006. Now he is a PhD of Northwestern Polytechnical University. His research interests are in the areas of network public opinion analysis and data mining, the modeling of complex systems.Huifeng Xue, he was born in Shanxi Yuncheng on June 16, 1964, received the PhD degree in Water resource economics from Xi'an Polytechnic in 1995. He is currently a professor in Northwestern Polytechnic University. His research interests are in the areas of the modeling of complex systems, Simulation and performance evaluation, management, systems engineering, energy and environmental systems engineering, computer control, intelligent control, network control.Zhe Yu is received the MS degree in Chinese Language and Literature from Northwestern University in 2005. She is currently a deputy director of information comprehensive office of Yan'an municipal committee. Her research interests are in the areas of Public Opinion Data Analysis, Data Mining, and Complex System Modeling.Feng Zhang is received the MS degree in Computer science from Xidian University in 2009. Now he is a PhD of Northwestern Polytechnical University. He is currently a professor in Yulin University. His research interests are in the areas of Cloud integrated manufacturing technology, the modeling of complex systems, the Internet of Things applications.

Abstract

Abstract: In order to improve the monitoring and early warning efficiency of network public opinion, and to reveal the spread of network public opinion, an evolution process model of public opinion in the Internet is proposed, It tries to analyze the evolution mechanism of network public opinion in the Internet, and provides certain theoretical support and method guidance for network public opinion monitoring and prediction. At the semantic level, the implementation method of knowledge fusion of different levels of public opinion information resources is proposed, which is supported by semantic technologies such as the semantic web in the big data environment. Then the multi-agent modeling and simulation method is used to establish the network public opinion information communication simulation model. The attributes of each participating entity were constructed in the model, and the influence of various factors on the network crisis information transmission was analyzed. The experimental results show that the proposed verification simulation model has high credibility. By analyzing the number of "opinion leaders" in the model, the participation of netizens, the credibility of the government, the speed of information disclosure and the transparency of the public, it can improve the monitoring and early warning efficiency of network public opinion, and better reveal the propagation law of network public opinion.

Key words: internet public opinion, semantic fusion, propagation model, information dissemination, complex social networks

Pengju Wang, Huifeng Xue, Zhe Yu, and Feng Zhang. Semantic Fusion and Propagation Model for Internet Public Opinion Data in Big Data Environment [J]. Int J Performability Eng, 2019, 15(12): 3099-3107.

Add to citation manager EndNote|Reference Manager|ProCite|BibTeX|RefWorks

References

[1] M. Ghiassi, J. Skinner,D. Zimbra, “Twitter Brand Sentiment Analysis: A Hybrid System using n-Gram Analysis and Dynamic Artificial Neural Network,” Expert Systems with Applications, Vol. 16, No. 1, pp. 6266-6282, February 2013
[2] F. Zhang, H. F. Xue,D. S. Xu, “Big Data Cleaning Algorithms in Cloud Computing,” International Journal of Online Engineering, Vol. 9, No. 3, pp. 77-81, March 2013
[3] J. Zhang and Y. Hong, “Opinion Evolution Analysis for Short-Range and Long-Range Deffuant-Weisbuch Models,” Physica A Statistical Mechanics and its Applications, Vol. 392, No. 21, pp. 5289-5297, April 2013
[4] A. C. R.Martins, “Continuous Opinions and Discrete Actions in Opinion Dynamics Prolems,” International Journal of Modern Physics C, Vol. 19, No. 4, pp. 617-624, April 2008
[5] N. Anstead and B. O'Loughlin, “Social Media Analysis and Public Opinion: The 2010 UK General Election,” Journal of Computer-Mediated Communication, Vol. 20, No. 2, pp. 204-220, March 2015
[6] J. Berndtsson, C. Dandeker,K. Ydén, “Swedish and British Public Opinion of the Armed Forces after a Decade of War,” Armed Forces and Society, Vol. 41, No. 2, pp. 307-328, February 2015
[7] J. Bolstad, “Dynamics of European Integration: Public Opinion in the Core and Periphery,” European Union Politics y, Vol. 16, No. 1, pp. 23-44, February 2015
[8] V. G.Donkal and K. Gyanendra, “A Multimodal Fusion based Framework to Reinforce IDS for Securing Big Data Environment using Spark,” Journal of Information Security and Applications, Vol. 43, No. 1, pp. 1-12, January 2018
[9] F. Zhang, H. F. Xue,J. -C. Zhang, “Multi-Source Big Data Dynamic Compressive Sensing and Optimization Method for Water Resources based on IoT,” Sustainable Computing: Informatics and Systems, Vol. 20, No. 6, pp. 210-219, July 2018
[10] J. Chae, D. Thom, Y. Jang, S. Y. Kim, T. Ertl,D. Ebert, “Public Behavior Response Analysis in Disaster Events Utilizing Visual Analytics of Microblog Data,” Computers and Graphics, Vol. 38, No. 2, pp. 51-60, February 2014
[11] S. P.Chang and B. K. Kaye, “The Tweet goes on: Interconnection of Twitter Opinion Leadership, Network Size, and Civic Engagement,” Computers in Human Behavior, Vol. 69, No. 6, pp. 174-180, June 2016
[12] J. D.Clinton and J. A. Grissom, “Public Information, Public Learning, and Public Opinion: Democratic Accountability in Education Policy,” Journal of Public Policy, Vol. 35, No. 3, pp. 355-385, April 2015
[13] S. Counts and K. E. Fisher, “Mobile Social Net Working as Information Ground: A Case Study,” Library and Information Science Research, Vol. 32, No. 2, pp. 98-115, February 2010
[14] E. Ellis, N. James,R. L. Jacobs, “Who Governs? Presidents, Public Opinion, and Manipulation,” Public Opinion Quarterly, Vol. 80, No. 1, pp. 212-215, February 2016
[15] J. A. Torkestani, “An Adaptive Focused Web Crawling Algorithm based on Learning Automata,” Applied Intelligence, Vol. 4, No. 1, pp. 586-601, January 2012
[16] R. Kontchakov, M. Rodríguez-Muro,M. Zakharyaschev, “Ontology-based Data Access with Databases: A Short Course,” Lecture Notes in Computer Science, Vol. 2, No. 2, pp. 194-229, February 2013
[17] H. Liu, K. H. Chai,J. F. Nebus, “Balancing Codification and Personalization for Knowledge Reuse: A Markov Decision Process Approach,” Journal of Knowledge Management, Vol. 17, No. 5, pp. 755-772, June 2013
[18] N. Ur-Rahman and J. A. Harding, “Textual Data Mining for Industrial Knowledge Management and Text Classification: A Business Oriented Approach,” Expert Systems with Applications, Vol. 39, No. 5, pp. 4729-4739, June 2012
[19] K. Sznajd-Weron and J. Sznajd, “Opinion Evolution in Closed Community,” Expert Systems with Applications, Vol. 11, No. 6, pp. 1157-1165, June 2000
[20] R. Hegselmann and U. Krause, “Opinion Dynamics and Bounded Confidence Models, Analysis, and Simulations,” Journal of Artifical Societies and Social Simulation (JASSS), Vol. 5, No. 3, pp. 1-33, April 2002

Semantic Fusion and Propagation Model for Internet Public Opinion Data in Big Data Environment

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 0

Recommended 0