Online public opinion prediction based on a novel seasonal grey decomposition and ensemble model

Due to the influence of netizens’ behaviors, social activities, media and other factors, the trend of online public opinion shows the characteristics of nonlinear and seasonal fluctuation, but most researchers ignored it. In order to accurately predict the hot-degree of online public opinion, this paper proposes an improved seasonal grey decomposition and ensemble model. The STL decomposition algorithm is used to decompose original public opinion data. And the grey modified exponential model is proposed based on the grey difference information. Then the dynamic seasonal factors and Bernoulli equation are introduced to establish the seasonal modified exponential grey Bernoulli model. The SMEGBM model is used to predict the seasonal sequence and trend sequence, and the ARIMA model is used to predict the remainder sequence. In order to validate the prediction effect of the new model, the hot-degree predication of “Lin Shengbin” and “Tangshan beating” online events are implemented for empirical analysis. Compared with other models, the model proposed in this paper shows higher prediction accuracy. The results show that it is necessary to take the periodicity into account in the establishment of network public opinion model. And the hybrid model. can provide theoretical supports for relevant departments to monitor and give early warning of sudden online public opinion events. • A novel hybrid grey seasonal model is proposed to predict online public opinion. • Based on the grey differential information principle, MEGM(1,1) model is proposed. • The dynamic seasonal factors that extracted from seasonal sequence are proposed. • The Bernoulli equation is introduced to the establishment of SMEGBM model. • Comparative studies illustrate the effectiveness of the SMEGBM-ARIMA model.