An Integrated Model Combining Grey Methods and Neural Networks and its Application to Burst Topic Tendency Prediction
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Abstract
Studying the development tendency of topics is an important part of online social network (OSN) analysis. To solve the problem of ad hoc topic popularity tendency prediction under insufficient effective samples, data sparsity and low accuracy of the prediction model, this study combines grey system theory with neural network method to propose a new model for topic tendency prediction. In this study, the grey relational analysis method is used to construct the social network topic popularity evaluation index system, and the topic popularity tendency is classified and weighted based on the grey proximity, and then the integrated system combining GM(1,1) model with BP neural network (BP-NN) model is established. Taking Sina Weibo’s burst topic data as an example, the effectiveness of the proposed model is verified. The experimental results show that the fusion model of proposed integrated model is better than a single independent prediction model, which can be effectively used to predict the trend of the popularity of a social network topic.