Internet Popular Topics Extraction of Traffic Content Words Correlation
Moe Key · Xi'an Jiaotong Daxue xuebao · 2007
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.