Research on trend prediction and evaluation of network public opinion

Network public opinion can reflect social and political attitude of people, and it is very important that we study trend prediction and evaluation of network public opinion, which can provide decision making for managers. Aiming at lacking in trend prediction and evaluation of network public opinion, the attributes of monitoring index system of network public opinion are reduced using rough set theory, and construct a more scientific monitoring index system of network public opinion combined with the real life, determine the weight of the index using the analytic hierarchy process, and present a novel method of trend prediction and evaluation of network public opinion on the basis of fuzzy comprehensive evaluation model from the aspects of the quantitative and qualitative. Example analysis shows that this method is simple and easy to implement. It helps to provide a new theory and practice guidance for trend prediction and risk evaluation of online network public opinion.

[1]  Shang Ru-na.  Index System of Online Public Opinion Cases Based on Improved Radar Chart , 2015 .

[2]  Huang Huixin.  Threat Evaluation Model of Internet Public Opinion Based on TOPSIS , 2012 .

[3]  Lin Chen.  Method of online public opinions pre-warning based on intuitionistic fuzzy reasoning , 2010 .

[4]  刘海鸥,et al.  Research on the Evaluation System of Microblogging Public Opinion Based on Vague Set , 2014 .

[5]  Lina Wang,et al.  Feature Weighting Fuzzy Clustering Integrating Rough Sets and Shadowed Sets , 2012, Int. J. Pattern Recognit. Artif. Intell..

[6]  Ling Yan,et al.  Research on Risk Quantification of Comprehensive Unit Price Based on Fuzzy Theory , 2013 .

[7]  Zhongzhi Shi,et al.  On quick attribute reduction in decision-theoretic rough set models , 2016, Inf. Sci..

[8]  Zhenmin Tang,et al.  On an optimization representation of decision-theoretic rough set model , 2014, Int. J. Approx. Reason..

[9]  Yu He.  Study on Strategic Environmental Assessment of Highway Construction Based on Entropy–AHP , 2013 .

[10]  Hua Li,et al.  A novel attribute reduction approach for multi-label data based on rough set theory , 2016, Inf. Sci..

[11]  Xiangfeng Luo,et al.  Sentiment Computing for the News Event Based on the Social Media Big Data , 2017, IEEE Access.

[12]  Wei-na Ji,et al.  Research on Construction Management Based on the WSR System Methodology , 2013 .

[13]  Xiangfeng Luo,et al.  Association Link Network Based Semantic Coherence Measurement for Short Texts of Web Events , 2017, J. Web Eng..

[14]  Yiyu Yao,et al.  Generalized attribute reduct in rough set theory , 2016, Knowl. Based Syst..

[15]  ZhaohaoWang.  Comparative Studies of Covering Rough Set Models , 2015 .

[16]  Degang Chen,et al.  An incremental algorithm for attribute reduction with variable precision rough sets , 2016, Appl. Soft Comput..

[17]  Haiyan Chen,et al.  The semantic analysis of knowledge map for the traffic violations from the surveillance video big data , 2015, Comput. Syst. Sci. Eng..

[18]  Dongyi Ye,et al.  A novel and better fitness evaluation for rough set based minimum attribute reduction problem , 2013, Inf. Sci..

[19]  Qinghua Hu,et al.  An improved attribute reduction scheme with covering based rough sets , 2015, Appl. Soft Comput..

[20]  Tingzhang Liu,et al.  A novel attribute reduction algorithm based on rough set and improved artificial fish swarm algorithm , 2016, Neurocomputing.

[21]  Wei De-Zhi,et al.  Internet public opinion chaotic prediction based on chaos theory and the improved radial basis function neural networks , 2015 .

[22]  Jun Fang,et al.  A fast feature selection approach based on rough set boundary regions , 2014, Pattern Recognit. Lett..

[23]  Pan Cheng-shen.  Study for AHP-based access strategy in distributed constellation network , 2014 .