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 publicationIntelligent Information and Database Systems - 4th Asian Conference, ACIIDS 2012, Proceedings
Pages477-486
Number of pages10
EditionPART 2
DOIs
Publication statusPublished - 2012 Mar 27
Event4th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2012 - Kaohsiung, Taiwan, Province of China
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)0302-9743
ISSN (Electronic)1611-3349

Conference

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

Keywords

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

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Yasumura, Y., Takahashi, H., & Uehara, K. (2012). Potential topics discovery from topic frequency transition with semi-supervised learning. In Intelligent Information and Database Systems - 4th Asian Conference, ACIIDS 2012, Proceedings (PART 2 ed., 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