Please wait a minute...
Data Analysis and Knowledge Discovery
Current Issue | Archive | Adv Search |
Research on prediction of public opinion reversal based on improved SMOTE algorithm and ensemble learning
Wang Nan,Li Hairong,Tan Shuru
(School of Management Science and Information Engineering, Jilin University of Finance and Economics, Changchun 130117, China)
Export: BibTeX | EndNote (RIS)      

[Objective] Based on the analysis of online public opinion events, determining their attribute characteristics and classification. When a new online public opinion event occurs, we can predict whether the event will reverse in advance, which can not only help the government adjust the direction of public opinion in time but also prevent the credibility of the government media from being negatively affected.[Methods] The representative online public opinion events were collected in the past five years. The improved SMOTE algorithm made a balance distribution treatment on the event data set, building the prediction model of online public opinion reversal based on the neural network ensemble learning, and using the indicators such as accuracy rate, recall rate to evaluate the prediction effect of the model. The latest online public opinion events in 2020 are selected to test the proposed model, so as to further reveal the internal mechanism of the constructed reversal prediction model. [Results] Through empirical research, the accuracy of the neural network ensemble learning classification model is 99% and the values of F and AUC are both 0.99, which verifies the feasibility and strong generalization performance of the model.

[Limitations] This paper only selects some characteristics of public opinion reversal events for research. Therefore, it cannot comprehensively represent all public opinion reversal events that will occur in the future. [Conclusions] The constructed public opinion reversal prediction model can accurately predict whether the public opinion event will reverse in advance.

Key words Online public opinion reversal      SMOTE algorithm      Neural network      Ensemble learning      Empirical study      
Published: 21 December 2020
ZTFLH:  G353  

Cite this article:

Wang Nan, Li Hairong, Tan Shuru. Research on prediction of public opinion reversal based on improved SMOTE algorithm and ensemble learning . Data Analysis and Knowledge Discovery, 0, (): 1-.

URL:     OR

[1] Yin Haoran,Cao Jinxuan,Cao Luzhe,Wang Guodong. Identifying Emergency Elements Based on BiGRU-AM Model with Extended Semantic Dimension[J]. 数据分析与知识发现, 2020, 4(9): 91-99.
[2] Qiu Erli,He Hongwei,Yi Chengqi,Li Huiying. Research on Public Policy Support Based on Character-level CNN Technology[J]. 数据分析与知识发现, 2020, 4(7): 28-37.
[3] Wang Sidi,Hu Guangwei,Yang Siyu,Shi Yun. Automatic Transferring Government Website E-Mails Based on Text Classification[J]. 数据分析与知识发现, 2020, 4(6): 51-59.
[4] Liu Weijiang,Wei Hai,Yun Tianhe. Evaluation Model for Customer Credits Based on Convolutional Neural Network[J]. 数据分析与知识发现, 2020, 4(6): 80-90.
[5] Wang Mo,Cui Yunpeng,Chen Li,Li Huan. A Deep Learning-based Method of Argumentative Zoning for Research Articles[J]. 数据分析与知识发现, 2020, 4(6): 60-68.
[6] Yan Chun,Liu Lu. Classifying Non-life Insurance Customers Based on Improved SOM and RFM Models[J]. 数据分析与知识发现, 2020, 4(4): 83-90.
[7] Su Chuandong,Huang Xiaoxi,Wang Rongbo,Chen Zhiqun,Mao Junyu,Zhu Jiaying,Pan Yuhao. Identifying Chinese / English Metaphors with Word Embedding and Recurrent Neural Network[J]. 数据分析与知识发现, 2020, 4(4): 91-99.
[8] Xu Yuemei,Liu Yunwen,Cai Lianqiao. Predicitng Retweets of Government Microblogs with Deep-combined Features[J]. 数据分析与知识发现, 2020, 4(2/3): 18-28.
[9] Xiang Fei,Xie Yaotan. Recognition Model of Patient Reviews Based on Mixed Sampling and Transfer Learning[J]. 数据分析与知识发现, 2020, 4(2/3): 39-47.
[10] Ni Weijian,Guo Haoyu,Liu Tong,Zeng Qingtian. Online Product Recommendation Based on Multi-Head Self-Attention Neural Networks[J]. 数据分析与知识发现, 2020, 4(2/3): 68-77.
[11] Peng Chen,Lv Xueqiang,Sun Ning,Zang Le,Jiang Zhaocai,Song Li. Building Phrase Dictionary for Defective Products with Convolutional Neural Network[J]. 数据分析与知识发现, 2020, 4(11): 112-120.
[12] Yu Bengong,Ji Haomin. Semi-Supervised Method for Text Classification Based on DW-TCI[J]. 数据分析与知识发现, 2020, 4(10): 58-69.
[13] Tao Yue,Yu Li,Zhang Runjie. Active Learning Strategies for Extracting Phrase-Level Topics from Scientific Literature[J]. 数据分析与知识发现, 2020, 4(10): 134-143.
[14] Bengong Yu,Yumeng Cao,Yangnan Chen,Ying Yang. Classification of Short Texts Based on nLD-SVM-RF Model[J]. 数据分析与知识发现, 2020, 4(1): 111-120.
[15] Weimin Nie,Yongzhou Chen,Jing Ma. A Text Vector Representation Model Merging Multi-Granularity Information[J]. 数据分析与知识发现, 2019, 3(9): 45-52.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938