A User-Based Modeling and Analysis of Network Public Opinion Using Particle Swarm Optimization

Abstract:

In the current researches on network public opinion, the events are mostly focused, instead of the users who are of great significance, but often ignored. Concerning this problem,the user-based event model encompassing the users and their concepts is introduced in this paper, and user concept clustering in spreading events is simulated with the speciation algorithm based particle swarm optimization (SPSO). According to the results of user concept clustering, the analysis of event dynamic evolution model is implemented. The clustering convergence velocity of users is controlled with the change of velocity parameter for subsequently coordinating event evolution, so that the recognition of network hot events is realized and the situation of emergencies is analyzed.Finally, the PSO-based and SPSO-based clustering behaviors of users are simulated, respectively. The experimental results show that SPSO can more effectively simulate the clustering behaviors of users in public opinion network and discover multiple user clustering centers.

• Periodical: Advanced Materials Research (Volumes 712-715)
• Pages: 2452-2457
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.712-715.2452
• Online since: June 2013
• Authors: Ding Yue Kan*, Rui Xin Ma
• Keywords: Mathematical Models, Network Public Opinion, Speciation Algorithm, Swarm Intelligence

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