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Topic Extraction on Twitter Considering Author’s Role Based on Bipartite Networks

  • Takako HashimotoEmail author
  • Tetsuji Kuboyama
  • Hiroshi Okamoto
  • Kilho Shin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10558)

Abstract

This paper proposes a quality topic extraction on Twitter based on author’s role on bipartite networks. We suppose that author’s role which means who were in what group, affects the quality of extracted topics. Our proposed method expresses relations between authors and words as bipartite networks, explores author’s role by forming clusters using our original community detection technique, and finds quality topics considering the semantic accuracy of words and author’s role.

Keywords

Topic extraction Social media analysis Twitter analysis Bipartite network Data mining Community detection 

Notes

Acknowledgment

This paper was supported by the Grant-in-Aid for Scientific Research (KAKENHI Grant Numbers 26280090, 15K00314, and 17H00762) from the Japan Society for the Promotion of Science.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Takako Hashimoto
    • 1
    Email author
  • Tetsuji Kuboyama
    • 2
  • Hiroshi Okamoto
    • 3
  • Kilho Shin
    • 4
  1. 1.Chiba University of CommerceIchikawaJapan
  2. 2.Gakushuin UniversityTokyoJapan
  3. 3.RIKEN Brain Science InstituteSaitamaJapan
  4. 4.University of HyogoKobeJapan

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