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

Authors:
Yoshiaki Yasumura;Hiroyoshi Takahashi;Kuniaki Uehara
Affiliations:
Shibaura Institute of Technology, Japan;Kobe University, Japan;Kobe University, Japan
Venue:
ACIIDS'12 Proceedings of the 4th Asian conference on Intelligent Information and Database Systems - Volume Part II
Year:
2012

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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.