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.
Highlights
Online public opinion, which is known as network public opinion, refers to the opinions or remarks with a certain influence and tendentiousness of netizen to the social public affairs, especially the hot social focuses through the Internet [1,2]
There are more and more studies focusing on the forecast of online public opinion, which can basically be divided into two categories: traditional forecasting method and modern forecasting method
According to the traditional forecasting method, the data of online public opinion is converted into time series, and the model is created by using the forecasting methods of autoregressive moving average, exponential smoothing and other time series
Summary
Online public opinion, which is known as network public opinion, refers to the opinions or remarks with a certain influence and tendentiousness of netizen to the social public affairs, especially the hot social focuses through the Internet [1,2]. The economic and social development of China is in a crucial stage, in which the various deeply rooted contradictions and problems arise day by day, so the hotspots of online public opinion are emerged one after another, which involve broad regions as well as extensive contents In such a situation, the negative online public opinion will have great negative impact on the national security and social stability, if the online public opinion cannot be guided and supervised correctly [4]. According to the traditional forecasting method, the data of online public opinion is converted into time series, and the model is created by using the forecasting methods of autoregressive moving average, exponential smoothing and other time series This method is simple and easy to be carried out. At last the performance of the model is verified by simulation experiment
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