流量内容词语相关度的网络热点话题提取

Internet Popular Topics Extraction of Traffic Content Words Correlation

针对网络舆情分析的需求，给出了网络热点话题定义及其形式化描述，分析了流量肉容中热点词语与热点话题的关系，提出了流量内容中热点词语的相关度计算算法．在此基础上，采用基于高密度连接区域的密度聚类方法得到热点词语簇，结合热点词簇相关的网页标题及网站地址信息，得出网络热点话题的属性描述．实验结果表明，该算法能够有效获取当前网络中的热点话题，

Aiming at the requirements of network public feeling analysis,the formal definition and description of the popular topic on Internet is presented,the relationship between hot words and popular topics is analyzed,and finally a hotpoint words correlation computing approach for extracting popular topics on Internet is introduced in traffic contents.Based on that,DBSCAN(Density-Based Spatical Clustering of Application with Noise) clustering algorithm is adopted to extract popular topics and formalized results are given.The test results show that this method has an availability of 16.7% in extracting Internet popular topics,which,compared to web mining and TDT(Topic Detection and Tracking),can provide a more suitable data source for effective recovery of Internet public opinions.