基于贝叶斯网络的食品安全舆情监控探针研究
作者:
作者单位:

作者简介:

通讯作者:

中图分类号:

基金项目:

国家重点研发计划(2017YFC1601800);贵州省科技计划(黔科合平台人才[2018]5404)


Research on Public Opinion Monitoring Probe on Food Safety Based on Bayesian Network
Author:
Affiliation:

Fund Project:

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 资源附件
  • |
  • 文章评论
    摘要:

    针对大数据时代食品安全舆情数据采集不够快捷与准确的问题,提出一种基于贝叶斯网络的食品安全舆情监控探针的研究方法.首先,通过MySQL数据库建立食品安全关键词库;其次,运用贝叶斯网络模型将关键词库构建形成监控探针,并选定人民众云舆情监测系统进行数据采集;第三,将监控探针与传统舆情数据采集、网络爬虫技术做3组对比实验(奶类、酒类、茶类),验证其有效性.结果显示3组实验的数据挖掘时间(乳制品类3 s;酒类2.5 s;茶类2.4 s)明显降低,数据有效率(乳制品类83.6%;酒类77%;茶类77.9%)明显升高.可见关键词库引入贝叶斯网络模型形成监控探针,可有效提高食品安全舆情数据采集的及时性与精准度.

    Abstract:

    To address the problem that the public opinion data collection on food safety is not fast and accurate enough in the era of big data, this study proposes a public opinion monitoring probe on food safety based on the Bayesian network. Firstly, the MySQL database is used to establish a food safety keyword database. Secondly, the Bayesian network model is adopted to build a monitoring probe with the keyword database, and the public opinion monitoring system of the “Zhongyun Big Data” of PeopleYun is chosen for data collection. Thirdly, the monitoring probe is compared with traditional data collection technologies on public opinions and Web crawler technologies in three groups of comparative experiments (milk, wine, and tea) to verify its effectiveness. The results show that the data mining time of the three groups of experiments (milk: 3 s; alcohol: 2.5 s; tea: 2.4 s) is significantly reduced, and the data efficiency (milk: 83.6%, alcohol: 77%, tea: 77.9%) is considerably enhanced. Therefore, introducing a keyword database into the bayesian network model to form a monitoring probe can effectively improve the timeliness and accuracy of public opinion data collection on food safety.

    参考文献
    相似文献
    引证文献
引用本文

王旎,孙晓红,吴锴,谢锋,陶光灿.基于贝叶斯网络的食品安全舆情监控探针研究.计算机系统应用,2022,31(1):29-36

复制
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:2021-03-22
  • 最后修改日期:2021-04-19
  • 录用日期:
  • 在线发布日期: 2021-12-17
  • 出版日期:
您是第位访问者
版权所有:中国科学院软件研究所 京ICP备05046678号-3
地址:北京海淀区中关村南四街4号 中科院软件园区 7号楼305房间,邮政编码:100190
电话:010-62661041 传真: Email:csa (a) iscas.ac.cn
技术支持:北京勤云科技发展有限公司

京公网安备 11040202500063号