The Application of Gray Model and Support Vector Machine in the Forecast of Online Public Opinion

Abstract

The forecast of online public opinion is a kind of complex forecasting problem with information, small sample and uncertainty. In order to improve the accuracy for the forecast of online public opinion, a new forecasting method based on a gray model and a support vector machine is proposed. The method comprises the steps of clustering the text, extracting the hotspots, aggregating the data and implementing other pretreatments of the network data, then creating a model GM (1, 1) for the time series of online public opinion, correcting the forecasting results of the model GM (1, 1) with a support vector machine, and then testing through a simulation experiment. The experimental results show that compared with traditional forecasting methods, the application of gray model and support vector machine improves the accuracy for the forecast of online public opinion. Moreover, a new method for the forecast of online public opinion is presented to some extent.