[Updated on Apr. 18] Integration of CiNii Articles into CiNii Research

Topic Estimation Method Using Base Selection

  • WAJIMA Koji
    Graduate School of Library, Information and Media Studies, University of Tsukuba
  • FURUKAWA Toshihiro
    Dept.Information and Computer Technology., Science University of Tokyo
  • SATOH Tetsuji
    Faculty of Library, Information and Media Science, University of Tsukuba

Bibliographic Information

Other Title
  • 基底選択を用いた話題性の評価
  • キテイ センタク オ モチイタ ワダイセイ ノ ヒョウカ

Search this article


<p> The social concern with communication between a consumer and maker has been growing for the last several years. A large number of unspecied users are investigating Consumer Generated Media's communication on the Internet. The claim has the tremendous impact on the purchasing behavior in Consumer Generated Media. This paper is intended as an investigation of signicant feature quantity for topic forecast. In this Paper, We investigate online media's topic forecast on Online Community article. We comprehensively extracted text information's feature quantity using existing research. Feature quantity is used, for 31 types and 2,071 dimensions in text information. Proposed Method consists of feature transformation and the base on evaluation using Nonnegative Matrix Factorization (NMF). The validity of the Proposed Method is veried by Support Vector Regression (SVR) and Classiers. It was found from the evaluative result that have an impact on view count. We report that research result.</p>


  • Joho Chishiki Gakkaishi

    Joho Chishiki Gakkaishi 29 (3), 193-212, 2019-10-15

    Japan Society of Information and Knowledge


See more



Report a problem

Back to top