Earticle

초록

영어

In order to enhance prediction precision of online public opinion, it put forward a kind of online public opinion prediction model (PSR-SVR) with the combination of chaos theory and support vector regression. First of all, the original data of online public opinion were obtained throughout topic segmentation, hotspot extraction, and data aggregate. Then, time sequence of online public opinion was reconstructed throughout phase-space reconstruction. Finally, the reconstructed time sequence of online public opinion was input support vector regression for modeling and prediction, and then it was compared with other online public opinion prediction model by experiment. The result shows that compared with the contrast model, PSR-SVR improves the prediction precision and reliability of online public opinion, and the prediction results have certain practical value.

목차

Abstract
 1. Introduction
 2. Phase Space Reconstruction and Support Vector Regression
  2.1. Phase Space Reconstruction
  2.2. Support Vector Regression
 3. PSR-SVR Online Public Opinion Trend Prediction Model
 4. Simulation Experiment
  4.1. Data Source
  4.2. Contrast Model and Evaluation Standard
  4.3. Pretreatment of Online Public Opinion
  4.4. Phase Space Reconstruction of Online Public Opinion Data
 5. Conclusion
 Reference

키워드

저자

• Song Liangong [ College of Information Engineering, North China University of Water Resources and Electric Power, Zhengzhou Henan, 450011 China ]
• Wu Huixin [ College of Information Engineering, North China University of Water Resources and Electric Power, Zhengzhou Henan, 450011 China ]
• Zhang Zezhong [ School of Water Conservancy, North China University of Water Resources and Electric Power, Zhengzhou Henan, 450011 China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Multimedia and Ubiquitous Engineering

• 간기

  월간

• pISSN

  1975-0080

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605