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https://doi.org/10.21256/zhaw-3524| Publication type: | Article in scientific journal |
| Type of review: | Peer review (publication) |
| Title: | Micro-text classification between small and big data |
| Authors: | Christen, Markus Niederberger, Thomas Ott, Thomas Aryobsei, Suleiman Hofstetter, Reto |
| DOI: | 10.1587/nolta.6.556 10.21256/zhaw-3524 |
| Published in: | Nonlinear Theory and Its Applications |
| Volume(Issue): | 6 |
| Issue: | 4 |
| Page(s): | 556 |
| Pages to: | 569 |
| Issue Date: | 2015 |
| Publisher / Ed. Institution: | IEICE |
| ISSN: | 2185-4106 |
| Language: | English |
| Subjects: | Mining; Text; Data; Clustering |
| Subject (DDC): | 006: Special computer methods |
| 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. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/4218 |
| Fulltext version: | Published version |
| License (according to publishing contract): | Licence according to publishing contract |
| Departement: | Life Sciences and Facility Management |
| Organisational Unit: | Institute of Computational Life Sciences (ICLS) |
| Appears in collections: | Publikationen Life Sciences und Facility Management |
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| File | Description | Size | Format | |
|---|---|---|---|---|
| 2015_ChristenEtAl_NOLTA.pdf | 381.66 kB | Adobe PDF |  View/Open |
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Christen, M., Niederberger, T., Ott, T., Aryobsei, S., & Hofstetter, R. (2015). Micro-text classification between small and big data. Nonlinear Theory and Its Applications, 6(4), 556–569. https://doi.org/10.1587/nolta.6.556
Christen, M. et al. (2015) ‘Micro-text classification between small and big data’, Nonlinear Theory and Its Applications, 6(4), pp. 556–569. Available at: https://doi.org/10.1587/nolta.6.556.
M. Christen, T. Niederberger, T. Ott, S. Aryobsei, and R. Hofstetter, “Micro-text classification between small and big data,” Nonlinear Theory and Its Applications, vol. 6, no. 4, pp. 556–569, 2015, doi: 10.1587/nolta.6.556.
CHRISTEN, Markus, Thomas NIEDERBERGER, Thomas OTT, Suleiman ARYOBSEI und Reto HOFSTETTER, 2015. Micro-text classification between small and big data. Nonlinear Theory and Its Applications. 2015. Bd. 6, Nr. 4, S. 556–569. DOI 10.1587/nolta.6.556
Christen, Markus, Thomas Niederberger, Thomas Ott, Suleiman Aryobsei, and Reto Hofstetter. 2015. “Micro-Text Classification between Small and Big Data.” Nonlinear Theory and Its Applications 6 (4): 556–69. https://doi.org/10.1587/nolta.6.556.
Christen, Markus, et al. “Micro-Text Classification between Small and Big Data.” Nonlinear Theory and Its Applications, vol. 6, no. 4, 2015, pp. 556–69, https://doi.org/10.1587/nolta.6.556.
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