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Associated Topic Extraction for Consumer Generated Media Analysis

  • Shinichi Nagano
  • Masumi Inaba
  • Yumiko Mizoguchi
  • Takahiro Kawamura
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4504)

Abstract

This paper proposes a new algorithm of associated topic extraction, which detects related topics in a collection of blog entries referring to a specified topic. It is a partial feature of our product reputation information retrieval service whose aim is to detect product names rather than general terms. 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 topic filtering, which extracts products relevant to or similar to a specified product. The paper also presents a brief evaluation of the algorithm, in comparison with TF-IDF. In respect to the evaluation, it can be concluded that the proposed algorithm can capture users’ impressions of associated topics more accurately than TF-IDF.

Keywords

Inverse Document Frequency Proper Noun Topic Detection International World Wide 12th International World Wide 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Shinichi Nagano
    • 1
  • Masumi Inaba
    • 1
  • Yumiko Mizoguchi
    • 1
  • Takahiro Kawamura
    • 1
  1. 1.Corporate R&D Center, Toshiba Corporation, Japan, 1, Komukai-Toshiba-cho, Saiwai-ku, Kawasaki-shi, 212-8582Japan

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