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Potential Topics Discovery from Topic Frequency Transition with Semi-supervised Learning

  • Conference paper
Intelligent Information and Database Systems (ACIIDS 2012)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7197))

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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.

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© 2012 Springer-Verlag Berlin Heidelberg

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Yasumura, Y., Takahashi, H., Uehara, K. (2012). Potential Topics Discovery from Topic Frequency Transition with Semi-supervised Learning. In: Pan, JS., Chen, SM., Nguyen, N.T. (eds) Intelligent Information and Database Systems. ACIIDS 2012. Lecture Notes in Computer Science(), vol 7197. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28490-8_50

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  • DOI: https://doi.org/10.1007/978-3-642-28490-8_50

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-28489-2

  • Online ISBN: 978-3-642-28490-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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