Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-3524
Title: Micro-text classification between small and big data
Authors : Christen, Markus
Niederberger, Thomas
Ott, Thomas
Aryobsei, Suleiman
Hofstetter, Reto
Published in : Nonlinear Theory and Its Applications
Volume(Issue) : 6
Issue : 4
Pages : 556
Pages to: 569
Publisher / Ed. Institution : IEICE
Issue Date: 2015
License (according to publishing contract) : Licence according to publishing contract
Type of review: Peer review (Publication)
Language : English
Subjects : Mining; Text; Data; Clustering
Subject (DDC) : 004: Computer science
Abstract: 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.
Further description : @IEICE 2015
Departement: Life Sciences und Facility Management
Organisational Unit: Institute of Applied Simulation (IAS)
Publication type: Article in scientific Journal
DOI : 10.1587/nolta.6.556
10.21256/zhaw-3524
ISSN: 2185-4106
URI: https://digitalcollection.zhaw.ch/handle/11475/4218
Appears in Collections:Publikationen Life Sciences und Facility Management

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