Potential topics discovery from topic frequency transition with semi-supervised learning

Yoshiaki Yasumura, Hiroyoshi Takahashi, Kuniaki Uehara

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

This paper presents a method for potential topic discovery from blogsphere. A potential topic is defined as an unpopular phrase that has potential to spread through many blogs. To discover potential topics, this method learns from topic frequency transitions in blog articles. Though this learning requires sufficient amount of labeled data, labeled data is costly and time consuming. Therefore this method employs a semi-supervised learning to reduce labeling cost. First, this method extracts candidates of potential topics from categorized blog articles. To detect potential topics from the candidates, a classifier is built from topic frequency transition data. Experimental results with real world data show the effectiveness of the proposed method.

本文言語English
ホスト出版物のタイトルIntelligent Information and Database Systems - 4th Asian Conference, ACIIDS 2012, Proceedings
ページ477-486
ページ数10
PART 2
DOI
出版ステータスPublished - 2012 3 27
イベント4th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2012 - Kaohsiung, Taiwan, Province of China
継続期間: 2012 3 192012 3 21

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
番号PART 2
7197 LNAI
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Conference

Conference4th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2012
CountryTaiwan, Province of China
CityKaohsiung
Period12/3/1912/3/21

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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