Presentation 2011-08-05
Text data mining of News about aftosa Panic
Muneo KUSHIMA, Nore MORRIS, Kenji ARAKI, Muneou SUZUKI,
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Abstract(in English) In the present study, articles relating to the aftosa panic were chosen among four newspapers printed within Japan. Specific articles that contained the KeyGraph, "aftosa", were selected and information regarding aftosa-related complications was analyzed qualitatively. Sentences were analyzed into morphemes, and the relations between characeristic vocabularies were analyzed by using a textual data mining technique (KeyGraph). Visualization of this information was attempted through text data mining with KeyGraph analysis. Analyzing newspapers by text data mining with KeyGraph can comprehend information qualitatively and visualize it. The result was a qualitative analysis of the aftosa panic using a textual data mining technique and the initial goal was achieved: a visual record of this information.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) Text data mining / KeyGraph / Aftosa / Newspaper / Co-occurence analysis network visualization
Paper # TL2011-9
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Committee TL
Conference Date 2011/7/29(1days)
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Paper Information
Registration To Thought and Language (TL)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Text data mining of News about aftosa Panic
Sub Title (in English)
Keyword(1) Text data mining
Keyword(2) KeyGraph
Keyword(3) Aftosa
Keyword(4) Newspaper
Keyword(5) Co-occurence analysis network visualization
1st Author's Name Muneo KUSHIMA
1st Author's Affiliation Sadowara High School()
2nd Author's Name Nore MORRIS
2nd Author's Affiliation Sadowara High School
3rd Author's Name Kenji ARAKI
3rd Author's Affiliation Faculty of Medicine, University of Miyazaki Hospital
4th Author's Name Muneou SUZUKI
4th Author's Affiliation Faculty of Medicine, University of Miyazaki Hospital
Date 2011-08-05
Paper # TL2011-9
Volume (vol) vol.111
Number (no) 170
Page pp.pp.-
#Pages 6
Date of Issue