The Research Of Micro-Blog Public Opinion Predictive Model Based On Optimized BP Neural Network

With the rapid development of Internet technology, micro-blog has become an important part of people’s lives. Public opinion caused by the micro-blog attracts more and more people’s attention. Since the micro-blog information’s propagation velocity is quickly, spread widely and arbitrary nature of micro-blog, so the information on the micro-blog has true and false. Positive and negative micro-blog public opinion will have different effects on people’s lives, some of the negative micro-blog public opinion will even constitute a crisis, then a serious impact on public safety. Therefore, the study on predicted micro-blog public opinion has a practical significance.Predicted micro-blog public opinion, we must get data which can be expressed micro-blog public opinion firstly. The paper uses the discrete time series to describe micro-blog public opinion’s trends. In this paper, we use Sina micro-blog platform for background, according the hot micro-blog topic text extraction, analysis, forecast micro-blog public opinion. Get time series step: one with Sina micro-blog API interface, access to micro-blog to get micro-blog text in some time; Second, according the characteristics of the corresponding pretreatment micro-blog text, use the methods of statistical micro-blog topic and found that micro-blog hot topic; Third, statistical the number of replies and forwarding number of micro-blog hot topic for some time, and use the number to consist of public opinion prediction model’s experimental data.BP neural network can be better fit the nonlinear variation of micro-blog public opinion’s time series, which can be used to predict the micro-blog public opinion, but there are some limitations:BP neural network’s learning algorithm has the weakness of forgetting the already learning samples. When there is noise in the sample, there may cause poor performance on BP neural network; BP neural network also has a slow speed of convergence, and easy to fall into local minima. We did two tasks: First, change the network’s structure to improve BP neural network. Behind the BP neural network input layer neurons, we add a layer to store the input layer’s history data. When the samples have a noise data, the layer can delay network parameters to improve the performance of BP neural network. The second is to use GSA to optimize the network parameters for BP neural network. GSA has fast convergence and will be better to avoid the problem of local minima, which make up the slow convergence speed of the BP neural network, easy to fall into local minimum values.In this paper, the time series of public opinion has obtained from the micro-blog, the comparative experiments were carried out in four public opinion prediction models. Experimental results show that GSA optimized public opinion prediction model which improved by BP neural network can achieve better prediction.