원문정보
보안공학연구지원센터(IJDTA)
International Journal of Database Theory and Application
Vol.9 No.8
2016.08
pp.201-210
피인용수 : 0건 (자료제공 : 네이버학술정보)
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
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
저자정보
참고문헌
자료제공 : 네이버학술정보