Curve fitting of the user barrage emotional change based on the hybrid kernel PSO_LSSVM model

Abstract

The prediction of the barrage emotional change is very important for video playback effect and the analysis of user interest. Currently, some existing method including least squares and BP network for data fitting were used. However, these methods often have "bulging phenomenon", poor applicability to small samples, and low generalization performance. In order to solve these problems, in this paper, we propose a hybrid kernel PSO_LSSVM model based on least squares support vector machine. The fitting performance of the model is mainly determined by the selected kernel function and its parameters. Considering that the local Gaussian radial basis kernel function has strong learning ability but weak generalization ability, while the global polynomial kernel function has strong generalization ability but weak learning ability. We propose to combine the advantages of the two, build a least squares support vector machine model based on hybrid kernels, and cited the particle swarm optimization algorithm to optimize twice to obtain the optimal parameter value of the model. Hence the model can achieve high fitting accuracy, and can also ensure a higher prediction accuracy. So as to obtain the fitting curve of the user's barrage emotion change, we carried out fitting experiments on the emotional data samples obtained from the barrage comment text, and conducted comparison experiments with unimproved least squares support vector machine, BP neural network and other methods. Verifying the effectiveness and generalization of the model in fitting the barrage emotional change curve.