摘要
【目的】针对现有预警体系多以企业自身和监管部门为主体、忽视网络舆情,导致预警力度不强、缺乏透明度及敏感性、使突发性安全问题时有发生且无法得到及时处理的现状,提出一种新的舆情预警模型。【方法】通过元搜索技术挖掘舆情信息,增加基准偏移值优化情感特征项倾向性权重,添加修正因子以改进潜在语义分析和支持向量机(LSA+SVM)算法,构建舆情分类预警模型。【结果】以多组突发性安全事件为例,应用Matlab进行仿真实验。结果证明该舆情预警模型切实可行,反应迅速,在语义维度为10时准确率可达85.75%。【局限】此方法对于能引起关注和讨论的安全事件更加有效。【结论】改进算法适用于舆情预警,可为企业和监管部门根据分类结果及时采取有效的预警措施提供合理化建议。
[Objective] This study proposes a new early warning model to track the public sentiment online, aiming to improve transparency and responding speed of the safety emergencies. [Methods] We used the modified LSA+SVM algorithm to build an early warning model, which retrieved public opinion data by meta search. [Results] We examined the new model with three different incidents, and found it was practical and fast. The precision rate was 85.75% when the semantic dimension was kept at 10. [Limitations] This method was more effective for the safety incidents drawing public attention and discussion. [Conclusions] The proposed algorithm helps us build an early warning system for public opinion, which provides suggestions to related companies and government organizations.
出处
《数据分析与知识发现》
CSSCI
CSCD
2017年第2期11-18,共8页
Data Analysis and Knowledge Discovery
基金
国家自然科学基金项目"高技术虚拟产业集群运行模式研究"(项目编号:70873029)
黑龙江省自然科学基金项目"高新技术企业物流模式选择技术研究"(项目编号:G201203)
黑龙江省博士后科研启动资金资助项目"黑龙江省制造企业动态联盟信誉保障机制研究"(项目编号:LBH-Q12065)的研究成果之一
关键词
潜在语义分析
支持向量机
舆情预警
情感倾向性分析
Latent Semantic Analysis(LSA) Support Vector Machine(SVM) Public Opinion Early Warning Emotional Orientation Analysis