[Objective] This paper analyzes online public opinion events to determine their attributes and classification. When an online public opinion event occurs, we can predict whether it will reverse in advance. This study not only helps the governments adjust the direction of public opinion in time but also protect the credibility of the governments and media. [Methods] First, we retrieved representative online public opinion events from the past five years. Then, we used the improved SMOTE algorithm to conduct a balance distribution treatment on the data set. Third, we built a prediction model for online public opinion reversal based on the neural network ensemble learning. Finally, we evaluated the model’s performance and internal mechanism with online public opinion events from 2020. [Results] The accuracy of the proposed model reached 99% and the F and AUC values were both 0.99. [Limitations] We only chose some characteristics from public opinion reversal events. Therefore, it cannot comprehensively represent all reversal events occurring in the future. [Conclusions] The constructed model can accurately predict whether or not a public opinion event will reverse.
王楠,李海荣,谭舒孺. 基于改进SMOTE算法与集成学习的舆情反转预测研究*[J]. 数据分析与知识发现, 2021, 5(4): 37-48.
Wang Nan,Li Hairong,Tan Shuru. Predicting of Public Opinion Reversal with Improved SMOTE Algorithm and Ensemble Learning. Data Analysis and Knowledge Discovery, 2021, 5(4): 37-48.
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