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Contribution Details

Type Journal Article
Scope Discipline-based scholarship
Title Micro-text classification between small and big data
Organization Unit
  • Markus Christen
  • Thomas Niederberger
  • Thomas Ott
  • Suleiman Aryobsei
  • Reto Hofstetter
Item Subtype Original Work
Refereed No
Status Published in final form
  • English
Journal Title Nonlinear Theory and Its Applications
Publisher Institute of Electronics, Information and Communication Engineers (IEICE)
Geographical Reach international
ISSN 2185-4106
Volume 6
Number 4
Page Range 556 - 569
Date 2015
Abstract Text Micro-texts emerging from social media platforms have become an important source for research. Automatized classification and interpretation of such micro-texts is challenging. The problem is exaggerated if the number of texts is at a medium level, making it too small for effective machine learning, but too big to be efficiently analyzed solely by humans. We present a semi-supervised learning system for micro-text classification that combines machine learning techniques with the unmatched human ability for making demanding, i.e. nonlinear decisions based on sparse data. We compare our system with human performance and a predefined optimal classifier using a validated benchmark data-set.
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Digital Object Identifier 10.1587/nolta.6.556
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