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Predicting Public Opinion Reversal Based on Evolution Analysis of Events and Improved KE-SMOTE Algorithm |
Wang Nan1,2,Li Hairong1(),Tan Shuru3 |
1School of Management Science and Information Engineering, Jilin University of Finance and Economics, Changchun 130117, China 2Institute of Economic Information Management, Jilin University of Finance and Economics, Changchun 130117, China3College of Information Science and Engineering, Guilin University of Technology, Guilin 541006, China |
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Abstract [Objective] This paper tries to accurately predict online public opinion reversal. [Methods] First, we retrieved the features of public opinion events based on their evolution characteristics and development process before the reversal points. Then, we used the improved KE-SMOTE algorithm to create an automatic optimization process, which balanced the event set with very skewed positive and negative samples. We also constructed a neural network ensemble learning model using the balanced event set. Finally, we examined our model with 30 trending public opinion events from 2021, and discussed the causes of errors for the inconsistent prediction results. We also provided corresponding countermeasures and suggestions on avoiding the reversal of public opinion. [Results] We found that the prediction accuracy of the proposed model on the test sets reached 99.7%, and all reversal events were predicted. [Limitations] While the time interval becoming much shorter between the occurrence and reversal of public opinion events, more research is needed to examine the proposed model with smaller data sets. [Conclusions] Our new model can accurately identify the public opinion reversal events in advance.
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Received: 05 August 2021
Published: 14 April 2022
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Fund:13th Five-year Plan for the Science and Technology Research Project of Education Department of Jilin Province(JJKH20210131KJ);13th Five-year Plan for the Key Fund Project of Education Science of Jilin Province(ZD20024);National Natural Science Foundation of China(61702213) |
Corresponding Authors:
Li Hairong,ORCID:0000-0002-4884-7783
E-mail: 1070929014@qq.com
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