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Open AccessArticle

SAW Classification Algorithm for Chinese Text Classification

School of Information Engineering, Northeast Dianli University, Jilin 132012, China
Database/Bioinformatics Laboratory, Chungbuk National University, Chungbuk 362-763, Korea
Author to whom correspondence should be addressed.
Academic Editor: Jason C. Hung
Sustainability 2015, 7(3), 2338-2352;
Received: 14 August 2014 / Revised: 18 December 2014 / Accepted: 1 February 2015 / Published: 27 February 2015
(This article belongs to the Special Issue Ubiquitous Green IT System for Sustainable Computing)
PDF [738 KB, uploaded 27 February 2015]


Considering the explosive growth of data, the increased amount of text data’s effect on the performance of text categorization forward the need for higher requirements, such that the existing classification method cannot be satisfied. Based on the study of existing text classification technology and semantics, this paper puts forward a kind of Chinese text classification oriented SAW (Structural Auxiliary Word) algorithm. The algorithm uses the special space effect of Chinese text where words have an implied correlation between text information mining and text categorization for high-correlation matching. Experiments show that SAW classification algorithm on the premise of ensuring precision in classification, significantly improve the classification precision and recall, obviously improving the performance of information retrieval, and providing an effective means of data use in the era of big data information extraction. View Full-Text
Keywords: big data; SAW classification algorithm; relevance big data; SAW classification algorithm; relevance

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Guo, X.; Sun, H.; Zhou, T.; Wang, L.; Qu, Z.; Zang, J. SAW Classification Algorithm for Chinese Text Classification. Sustainability 2015, 7, 2338-2352.

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