Name
Abstract | ||
|---|---|---|
Blogs, or weblogs, have rapidly gained in popularity over the past decade. Because of the huge volume of existing blog posts, information in the blogosphere is difficult to access and retrieve. Existing studies have focused on analyzing personal blogs, but few have looked at corporate blogs, the numbers of which are dramatically rising. In this paper, we use probabilistic latent semantic analysis to detect keywords from corporate blogs with respect to certain topics. We then demonstrate how this method can represent the blogosphere in terms of topics with measurable keywords, hence tracking popular conversations and topics in the blogosphere. By applying a probabilistic approach, we can improve information retrieval in blog search and keywords detection, and provide an analytical foundation for the future of corporate blog search and mining. |
Year | Venue | Keywords |
|---|---|---|
2007 | DMBiz@PAKDD | blog search,corporate blogs,probabilistic latent semantic analysis,information retrieval,corporate blog search,analytical foundation,blog post,keywords detection,personal blogs,probabilistic approach,web mining |
Field | DocType | Citations |
World Wide Web,Web mining,Computer science,Popularity,Probabilistic latent semantic analysis,Blogosphere,Probabilistic logic,Spam blog | Conference | 2 |
PageRank | References | Authors |
0.45 | 8 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Flora S. Tsai | 1 | 352 | 23.96 |
| Yun Chen | 2 | 49 | 2.53 |
| Kap Luk Chan | 3 | 1039 | 77.99 |