金海

• 1:重庆师范大学涉外商贸学院

摘要(Abstract)：

针对传统预测系统一直存在预测结果不准确、系统稳定性差的问题,提出并设计了基于改进神经网络算法的微博热点预测系统,其硬件部分主要对数据采集模块、微博信息传播趋势分析模块、微博热点判别模块进行了分析并设计,软件部分主要引进了改进神经网络算法,对原有系统进行了优化。实验结果表明,采用改进系统对微博热点进行预测时,其预测稳定性相比传统预测系统要优越,在相同时间内,出现波动的次数降低了2~4次,具有一定的优势。

关键词(KeyWords)： 微博热点;预测系统;改进神经网络算法;数据采集;微博信息传播;预测稳定性

Abstract:

Keywords:

基金项目(Foundation): 重庆师范大学涉外商贸学院重点科研项目(KY2015002)~~

作者(Author): 金海

DOI: 10.16652/j.issn.1004-373x.2018.12.038

参考文献(References)：

• [1]郑志蕴,江国林,张行进,等.基于多特征的热门微博预测算法研究[J].小型微型计算机系统,2017,38(3):494-498.ZHENG Zhiyun,JIANG Guolin,ZHANG Xingjin,et al.Research on the prediction algorithm for Sina popular micro blog based on multi-features[J].Journal of Chinese computer systems,2017,38(3):494-498.
• [2]何炎祥,刘健博,刘楠,等.基于改进人口模型的微博话题趋势预测[J].通信学报,2015,36(4):5-12.HE Yanxiang,LIU Jianbo,LIU Nan,et al.Based on improved Malthusian model microblogging topic trend forecast[J].Journal on communications,2015,36(4):5-12.
• [3]姬建新.捕鱼算法优化核极限学习机的微博热点话题预测[J].激光杂志,2015,36(1):128-131.JI Jianxin.Hot topic prediction of micro-blog based on kernel extreme learning machine and fishing algorithm[J].Laser journal,2015,36(1):128-131.
• [4]阳馨.基于信息功率谱的微博热点挖掘模型[J].兰州理工大学学报,2017,43(3):106-111.YANG Xin.Micro-blog hot-spots mining model based on information power spectrum[J].Journal of Lanzhou University of Technology,2017,43(3):106-111.
• [5]张贵红,李中华.基于数据挖掘技术的微博热点话题预测[J].现代电子技术,2017,40(15):52-55.ZHANG Guihong,LI Zhonghua.Micro-blog hot topic forecast-ing based on data mining technology[J].Modern electronics technique,2017,40(15):52-55.
• [6]齐银峰,谭荣建.基于改进粒子群优化算法的BP神经网络在大坝变形分析中的应用[J].水利水电技术,2017,48(2):118-124.QI Yinfeng,TAN Rongjian.Application of improved particle swarm optimization algorithm-based BP neural network to dam deformation analysis[J].Water resources and hydropower engineering,2017,48(2):118-124.
• [7]刘业政,杜亚楠,姜元春,等.基于热度曲线分类建模的微博热门话题预测[J].模式识别与人工智能,2015,28(1):27-34.LIU Yezheng,DU Yanan,JIANG Yuanchun,et al.Trend prediction for microblog based on classification modeling of heat curves[J].Pattern recognition and artificial intelligence,2015,28(1):27-34.
• [8]廖祥文,张丽瑶,宋志刚,等.基于卷积神经网络的中文微博观点分类[J].模式识别与人工智能,2016,29(12):1075-1082.LIAO Xiangwen,ZHANG Liyao,SONG Zhigang,et al.Chinese microblog sentiment classification based on convolutional neural network[J].Pattern recognition and artificial intelligence,2016,29(12):1075-1082.
• [9]刘龙飞,杨亮,张绍武,等.基于卷积神经网络的微博情感倾向性分析[J].中文信息学报,2015,29(6):159-165.LIU Longfei,YANG Liang,ZHANG Shaowu,et al.Convolutional neural networks for Chinese micro-blog sentiment analysis[J].Journal of Chinese information processing,2015,29(6):159-165.