An Online Review Research Based on Opinion Dynamics  Model

Zizhan Lin; Jinlian Zhou; Ye Wu; Jinghua Xiao

doi:10.18282/inm.v4i1.318

Zizhan Lin School of Science, Beijing University of Posts and Telecommunications, Beijing 100876, China
Jinlian Zhou School of Journalism and Communication, Beijing Normal University, Beijing 100875, China
Ye Wu School of Journalism and Communication, Beijing Normal University, Beijing 100875, China
Jinghua Xiao School of Journalism and Communication, Beijing Normal University, Beijing 100875, China

Article ID: 318

621 Views, 42 PDF Downloads

DOI: https://doi.org/10.18282/inm.v4i1.318

Keywords: Complex Network, Opinion Dynamics, Hegselmann-Krause Model, Online Review, Opinion Evolution

Abstract

Due to the rapid growth of Internet, consumers obtain information about products and experiences from online rating platforms, which further influences their purchase decisions. Considering the differences in the opinion interaction pattern, traditional opinion dynamics models cannot explain individual review behaviors. In order to explore the evolution process of opinions on the online review platform, we optimized the network structure and confidence thresholds of traditional models based on the collected data from Dianping, and created an opinion dynamics model for online rating platforms. The proposed method can advance the understanding for the evolution process of online opinion.

References

By M, Huang F. Movie Online Review Forecast Based on Viewpoint Dynamics. Modern Computer China Xunkan. 2016; (2): 7-11.

Su J, Liu B, Li Q, et al. Prediction of Number of Online Comments Based on Viewpoint Dynamics. Computer Engineering. 2014; 40(10): 155-160.

Wan Y, Ma B, Pan Y. Opinion evolution of online consumer reviews in the e-commerce environment. Electronic Commerce Research. 2018; 18(2): 291-311.

Sznajd-Weron K, Sznajd J. Opinion evolution in closed community. International Journal of Modern Physics. 2000; 11(06): 1157-1165.

Howe DA, Percival DB. Wavelet variance, Allan variance, and leakage. IEEE transactions on instrumentation and measurement. 1995; 44(2): 94-97.

Holley RA, Liggett TM. Ergodic theorems for weakly interacting infinite systems and the voter model. The annals of probability. 1975; 643-663.

Deffuant G, Neau D, Amblard F, et al. Mixing beliefs among interacting agents. Advances in Complex Systems. 2000; 3(01n04): 87-98.

Hegselmann R, Krause U. Opinion dynamics and bounded confidence models, analysis, and simulation. Journal of artificial societies and social simulation. 2002; 5(3): 1-24.

Proskurnikov AV, Tempo R. A tutorial on modeling and analysis of dynamic social networks. Part II. Annual Reviews in Control. 2018; 45: 166-190.

Meng XF, Van Gorder RA, Porter MA. Opinion formation and distribution in a bounded-confidence model on various networks. Physical Review E. 2018; 97(2): 022312.

Li D, Han D, Ma J, et al. Opinion dynamics in activity-driven networks. EPL (Europhysics Letters). 2018; 120(2): 28002.

Perez IJ, Cabrerizo FJ, Alonso S, et al. On dynamic consensus processes in group decision making problems. Information Sciences. 2018; 459: 20-35.

Gou Z, Fan M, Wang G. Research on Evolution of Continuous Views Without Trust Boundary Limits in Complex Networks. Journal of University of Electronic Science and Technology. 2015; 44(5): 749-756.

Kan J, Xie J, Zhang H. Analysis of the Influence of Social Strengthening Effect and Linking Weight on Network Information Dissemination. University of Electronic Science and Technology Newspaper. 2014; 43(1): 21-25.

Lee J, Park DH, Han I. The effect of negative online consumer reviews on product attitude: An information processing view. Electronic commerce research and applications. 2008; 7(3): 341-352.

Ghose A, Ipeirotis PG. Estimating the helpfulness and economic impact of product reviews: Mining text and reviewer characteristics IEEE Transactions on Knowledge and Data Engineering. 2011; 23(10): 1498-1512.

Sean, Y, Li X. Defuant-I Public Opinion Communication Model with Individual Information Difference. Systems Engineering. 2017; (6): 82-88.

Jindal N, Liu B. Opinion spam and analysis. In: Proceedings of the 2008 international conference on web search and data mining. ACM; 2008. pp. 219-230.