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

Yoshiaki Yasumura, Hiroyoshi Takahashi, Kuniaki Uehara

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages477-486
Number of pages10
Volume7197 LNAI
EditionPART 2
DOIs
Publication statusPublished - 2012
Event4th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2012 - Kaohsiung
Duration: 2012 Mar 192012 Mar 21

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume7197 LNAI
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other4th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2012
CityKaohsiung
Period12/3/1912/3/21

Fingerprint

Blogs
Supervised learning
Labeling
Classifiers
Costs

Keywords

  • potential topic
  • semi-supervised learning
  • topic frequency transition
  • Web mining

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Yasumura, Y., Takahashi, H., & Uehara, K. (2012). Potential topics discovery from topic frequency transition with semi-supervised learning. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (PART 2 ed., Vol. 7197 LNAI, pp. 477-486). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7197 LNAI, No. PART 2). https://doi.org/10.1007/978-3-642-28490-8_50

Potential topics discovery from topic frequency transition with semi-supervised learning. / Yasumura, Yoshiaki; Takahashi, Hiroyoshi; Uehara, Kuniaki.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 7197 LNAI PART 2. ed. 2012. p. 477-486 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7197 LNAI, No. PART 2).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Yasumura, Y, Takahashi, H & Uehara, K 2012, Potential topics discovery from topic frequency transition with semi-supervised learning. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). PART 2 edn, vol. 7197 LNAI, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 2, vol. 7197 LNAI, pp. 477-486, 4th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2012, Kaohsiung, 12/3/19. https://doi.org/10.1007/978-3-642-28490-8_50
Yasumura Y, Takahashi H, Uehara K. Potential topics discovery from topic frequency transition with semi-supervised learning. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). PART 2 ed. Vol. 7197 LNAI. 2012. p. 477-486. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2). https://doi.org/10.1007/978-3-642-28490-8_50
Yasumura, Yoshiaki ; Takahashi, Hiroyoshi ; Uehara, Kuniaki. / Potential topics discovery from topic frequency transition with semi-supervised learning. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 7197 LNAI PART 2. ed. 2012. pp. 477-486 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2).
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