摘要
为了应对微信息舆情数据的格式复杂、价值稀疏和收集困难等大数据处理技术难题,基于隐含语义分析和粗糙集近似约简理论,设计微信息的数据区间值集和近似匹配分类算法.在不影响数据主要关联关系的原则下,提炼核心属性、消减次要属性,实现一种微信息异常主题倾向的发现方法.结果表明,该近似约简算法能在完成微信息兴趣倾向主题分类的前提下,将数据集属性大幅度缩减,提高微信息的信息挖掘效率,为微信息大数据舆情处理工作提供了新的思路和案例.
In order to deal with such technological problems in big data processing as complex format, sparse value and difficult collection of micro-message public opinion data, based on the latent semantic analysis （LSA） and rough set approximate reduction theory, the data interval value set and approximate matching classification algorithm of micro-message were designed. Under the principle of not affecting the main association relationship of data, the core attributes were extracted, the secondary attributes were reduced, and a method of discovering the micro-message abnormal theme tendency was realized. The results show that under the premise of completing the classification of micro-message interest tendency themes, the proposed approximate reduction algorithm can greatly reduce the data set properties, improve the information mining efficiency of micro-message, and provide a new thought and case for the processing work of public opinion of micro-message big data.
出处
《沈阳工业大学学报》
EI
CAS
北大核心
2016年第3期309-313,共5页
Journal of Shenyang University of Technology
基金
教育部规划课题资助项目(14YJA860017)
关键词
大数据
微信息
近似约简
粗糙集
隐含语义分析
主题发现
区间值
近似集
big data
micro-message
approximate reduction
rough set
latent semantic analysis
theme discovery
interval value
approximation set
