基于支持向量机的网络舆情混沌预测

精确预测网络舆情发展趋势，对防止负面网络舆情对公共安全威胁具有重要意义，针对网络舆情变化的时变性、混沌性，提出一种基于支持向量机的网络舆情混沌预测模型（PHR-SVR）。证明了网络舆情具有混沌特性，根据Takens定理分别采用互信息法和G-P法确定延迟时间和嵌入维数重构网络舆情时间序列相空间；在相空间中，利用支持向量回归机（SVR）建立网络舆情预测模型，与其他预测模型进行对比实验。结果表明，相对于对比模型，PHR-SVR提高了网络舆情的预测精度和可靠性，预测结果具有一定实用价值。

Internet public opinion chaotic prediction based on Support Vector Regression machine

In order to improve the prediction accuracy of internet public opinion, this paper proposes an mternet pulgnc opinion prediction model based on chaotic theory and Support Vector Regression. The internet public opinion time series proves to be with chaos characteristics, and then delay time and embedding dimension are calculated using mutual information method and GP method respectively according to takens theorem, and the internet public opinion time series is reconstructed in phase space. The internet public opinion forecasting model is established using Support Vector Regression, and the simulation experiment is carried out with comparison models. The experimental results show that, compared with other models, the proposed model has improved the prediction accuracy and stability of internet public opinion and the prediction results have practical value.