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Contribution Details
| Type | Journal Article |
| Scope | Discipline-based scholarship |
| Title | Micro-text classification between small and big data |
| Organization Unit | |
| Authors |
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| Item Subtype | Original Work |
| Refereed | No |
| Status | Published in final form |
| Language |
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| 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. |
| Free access at | DOI |
| Official URL | https://www.jstage.jst.go.jp/article/nolta/6/4/6_556/_pdf |
| Related URLs |
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| Digital Object Identifier | 10.1587/nolta.6.556 |
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