Shinichi Nagano, Masumi Inaba, Yumiko Mizoguchi, Takahiro Kawamura
This paper proposes a new algorithm of associated topic extraction, which detects related topics in a collection of blog entries commenting on a specified topic. The main feature of the algorithm is to evaluate how important a topic is to the collection, according to the popularity of blog entries through Trackbacks and comments. Another feature is to utilize product ontology for excluding unrelated topics. Evaluation results show that the proposed algorithm can capture users' impressions of associated topics more accurately than TF-IDF.
Subjects: 1.10 Information Retrieval; 10. Knowledge Acquisition
Submitted: Feb 15, 2008