Earticle

초록

영어

Prediction of network public opinion is a complicated prediction featuring poor information, small samples and uncertainty. A prediction model of network public opinion based on grey support vector machine (SVM) is specified to increase prediction accuracy. First, network data are preprocessed by text clustering, hotpot extraction and data aggregation. Then a time series model GM(1,1) is established and SVM is used to modify prediction outcomes of GM(1,1). At last, simulation experiment is conducted to test performance of the model. Simulation results indicate that grey SVM improves the prediction accuracy of network public opinion compared with traditional prediction models. The predictions have certain practical values.

목차

Abstract
 1. Introduction
 2. Preprocessing of Network Public Opinion Data
  2.1. Text Clustering
  2.2. Hotpot Abstraction
  2.3. Data Aggregation
 3. Grey SVM Model
  3.1. GM (1,1) Model
  3.2. SVM Model
  3.3. Workflow of Prediction Model of Network Public Opinion
 4. Simulation Experiment
  4.1. Data Source
  4.2. Data Preprocessing
  4.3. GM(1,1) Prediction
  4.4. Modification of Residual by SVM
  4.5. Performance Comparison with Other Models
 5. Conclusions
 References

키워드

저자

• Bo Li [ Changchun University of Science and Technology, Changchun Institute of Technology ]
• BaoXing Bai [ Changchun University of Science and Technology ]
• Changsheng Zhang [ College of Information Science & Engineering, Northeastern University ]
• Yixue Jiang [ Changchun Institute of Technology ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Database Theory and Application

• 간기

  격월간

• pISSN

  2005-4270

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605