基于改进狼群算法优化LSTM网络的舆情演化预测

Public Opinion Evolution Prediction Based on LSTM Network Optimized by an Improved Wolf Pack Algorithm

为提高预测舆情演化趋势的能力,提出了一种基于改进狼群算法(IWPA)优化长短期记忆(LSTM)神经网络的舆情演化预测模型。采用Halton Sequence进行初始化,提高种群多样性;设计步长因子进行高斯-正弦扰动变换,提高狼群探索开发能力;结合鲸鱼优化算法中的螺旋改进围攻机制,增强狼群的局部搜索能力;引入记忆力机制,使用双向记忆种群增加狼群协同合作能力,将改进后的狼群算法应用到LSTM神经网络的超参数预测。采用“新冠疫情”和“食品安全”等关键词作为实例,证明了IWPA-LSTM神经网络舆情演化预测模型具有良好的准确性和普适性,适用于多种舆情演化的预测。

To improve the ability to predict the evolution trend of public opinion,a public opinion evolution trend prediction model based on an improved wolf pack algorithm and optimized long-short term memory neural network is proposed.Use Halton Sequence to initialization to improve population diversity.Design step factor to perform Gauss-Sine perturbation transformation to improve wolf group exploration and development capabilities.Combine with the spiral in the whale optimization algorithm to improve the siege mechanism to enhance the local search ability of wolves.The bidirectional memory population is used to increase the cooperative ability of the wolf pack.The improved wolf pack algorithm(IWPA)is applied to the hyperparameter prediction of the LSTM neural network.Using keywords such as“COVID-19”and“Food Safety”,the experiment proves that the IWPA-LSTM neural network public opinion evolution prediction model has good accuracy and generality.The model is suitable for the prediction of various public opinion evolution trends.