基于改进神经网络算法的微博热点预测系统设计
|
摘要
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。
In allusion to the long-existing problems of inaccurate prediction results and poor system stability of the traditional prediction system,a micro-blog hot spot prediction system based on improved neural network algorithm is proposed and designed. For the hardware part of the system,the data acquisition module,micro-blog information propagation trend analysis module,and micro-blog hot spot discrimination module are mainly analyzed and designed. For the software part,the improved neural network algorithm is mainly introduced to optimize the original system. The experimental results show that the improved micro-blog hot spot prediction system has superior prediction stability than the traditional prediction system,and the number of fluctuations is reduced by 2~4 times within the same time,which has a certain advantages.
In allusion to the long-existing problems of inaccurate prediction results and poor system stability of the traditional prediction system,a micro-blog hot spot prediction system based on improved neural network algorithm is proposed and designed. For the hardware part of the system,the data acquisition module,micro-blog information propagation trend analysis module,and micro-blog hot spot discrimination module are mainly analyzed and designed. For the software part,the improved neural network algorithm is mainly introduced to optimize the original system. The experimental results show that the improved micro-blog hot spot prediction system has superior prediction stability than the traditional prediction system,and the number of fluctuations is reduced by 2~4 times within the same time,which has a certain advantages.
引文
[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.
[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.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |