論文

査読有り
2017年

Topic extraction on twitter considering author’s role based on bipartite networks

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
  • Takako Hashimoto
  • ,
  • Tetsuji Kuboyama
  • ,
  • Hiroshi Okamoto
  • ,
  • Kilho Shin

10558
開始ページ
239
終了ページ
247
記述言語
英語
掲載種別
研究論文(国際会議プロシーディングス)
DOI
10.1007/978-3-319-67786-6_17
出版者・発行元
Springer Verlag

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.

リンク情報
DOI
https://doi.org/10.1007/978-3-319-67786-6_17
DBLP
https://dblp.uni-trier.de/rec/conf/dis/HashimotoKOS17
URL
http://dblp.uni-trier.de/db/conf/dis/dis2017.html#conf/dis/HashimotoKOS17
ID情報
  • DOI : 10.1007/978-3-319-67786-6_17
  • ISSN : 1611-3349
  • ISSN : 0302-9743
  • DBLP ID : conf/dis/HashimotoKOS17
  • SCOPUS ID : 85030256645

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