大数据环境下微博舆情热点话题挖掘方法研究

Study on the Method of Micro- blogging Public Opinion Hotspots Mining in Big Data

通过分析大数据环境下微博舆情的发展特点和舆情自动监测的具体需求,设计了微博舆情热点挖掘系统结构模型,描述了各层的主要功能和实现方法。然后讨论了热点话题发现的方法,首先运用ICTCLAS和Ant Conc等工具提取热点词,其次描述规范化的数据表示形式,最后通过Chameleon聚类算法实现热点博文的聚类和话题抽取。该方法将对及时发现敏感信息和掌握舆情热点提供信息支持。

Based on the analysis of the development characteristics and the specific requirements for the automatic monitoring of the micro- blogging public opinion in big data,this paper presented a general system model for the hotspots mining,in which the main functions and implementation of the levels are described,and then followed the discussion of the mining methods. The proposed method consisted of three parts： first,hot words are extracted by ICTCLAS and Ant Conc;second,the standardization is performed for the data; third,the hotspots are clustered and the topics can be extracted by the Chameleon method. The presented method has potential applications and provides supports in the discovery of hotspots.