基于模糊神经网络的微博舆情趋势预测方法

发布时间：2018-04-02 10:28

本文选题：粒子群优化算法　切入点：模糊神经网络　出处：《情报科学》2017年12期

【摘要】：【目的/意义】微博舆情对社会各领域的影响与日俱增,但由于其影响因素众多,呈现出非线性且复杂的变化。因此,如何快速、准确地预测其发展趋势是一个很有价值的研究课题。【方法/过程】以微博话题的博文总数作为微博话题发展趋势的量化指标,考虑话题发展的复杂性和非线性的特点,采用模糊神经网络来预测微博话题的发展趋势。并通过改进的粒子群优化算法对模糊神经网络的参数进行优化以更好的发挥模糊神经网络在处理非线性、模糊性等复杂问题上的优越性。【结果/结论】通过对新浪微博数据集的对比实验,验证了本文所提方法的有效性和准确性。本文方法有效解决了微博舆情趋势预测中遇到的模型参数复杂、易陷入局部最优的问题,提高了微博舆情发展趋势预测的准确性。
[Abstract]:The influence of Weibo's public opinion on every field of society is increasing day by day, but because of its many influential factors, it presents nonlinear and complex changes. It is a valuable research topic to predict its development trend accurately. [methods / process] take the total number of blog articles on Weibo topic as the quantitative index of the developing trend of Weibo topic, and consider the complexity and nonlinear characteristics of topic development. Fuzzy neural network is used to predict the development trend of Weibo topic, and the parameters of fuzzy neural network are optimized by the improved particle swarm optimization algorithm in order to make better use of fuzzy neural network to deal with nonlinearity. The advantages of fuzziness and other complex problems. [results / conclusions] A comparative experiment on the data sets of Weibo, Sina, The effectiveness and accuracy of the method proposed in this paper is verified. The method solves the problem of complex model parameters and local optimum in forecasting the trend of public opinion of Weibo, and improves the accuracy of forecasting the trend of public opinion development of Weibo.
【作者单位】：西安电子科技大学经济与管理学院;
【分类号】：G206;TP393.092

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