Non-topical classification of healthcare information on the web

Sachio Hirokawa, Emi Ishita

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

1 Citation (Scopus)


The present paper collected the asthma related 4,762 Web pages from 1,759 sites using 6 queries. Each site is manually categorized by the standard topics of description and information dissemination, diary and idle talk and Q&A. By careful analysis, it turned out that the pages can be classified in non-topical categories such as 'reading level', 'objectivity/subjectivity' and 'reliability'. The manually assigned labels of non-topical categories are then used as learning data to apply SVM (support machine vector). The prediction performance (F-measure) were below 50% with the naive application of SVM. However, the prediction performance was improved over 50% by feature selection except for reading level.

Original languageEnglish
Title of host publicationSmart Digital Futures 2014
PublisherIOS Press
Number of pages11
ISBN (Print)9781614994046
Publication statusPublished - 2014

Publication series

NameFrontiers in Artificial Intelligence and Applications
ISSN (Print)0922-6389

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence


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