Abstract

This study proposes a food complaint text early warning method based on the guidance of ontology and establishes a scientific and reasonable system of early warning, builds and improves the food security early warning platform. All of those make this study play a supplementary role in the research content of food safety regulators. Based on traditional early warning system, this study constructs food safety complaints warning platform model and builds the food domain ontology and expands food complaint document semantics to highlight the implicit semantics and improve the document's semantic accuracy. Through the calculation of similarity of theme characteristic vector and text categorization constructing classifier, make the automatic classification of food complaint documents based on the theme come true for those which are not correctly classified documents for unsupervised clustering, which can be the purpose of food safety alarm. Then, it is possible to use complaints about food safety for rapid and accurate text data processing, make the food safety regulators understand the food safety hidden trouble in time to protect consumers' rights and interests.

Keywords:

Food safety, text classification, text clustering, text,

References

1. Berrueta, L.A., R.M. Alonso-Salces and K. Heberger, 2007. Supervised pattern recognition in food analysis. J. Chromatogr. A, 1158(1): 196-214.
   CrossRef    PMid:17540392    Direct Link
2. Christopher, D.M. and S. Hinrich, 1999. Foundations of Statistical Natural Language Processing. The MIT Press, Cambridge, ISBN: 0262133601, pp: 680.
   Direct Link
3. Food Security Law of the People's Republic of China, 2009. Part Ten by Low, Article Ninety-nine, 11(6): 29-38. (In Chinese)
   CrossRef    Direct Link
4. Likas, A., N. Vlassis and J. Verbeek, 2013. The global k-means clustering algorithm. Pattern Recogn., 36(2): 451-461.
   CrossRef    Direct Link
5. Mu-Hee, S., L. Soo-Yeon, K. Dong-Jin and L. Sang-Jo, 2005. Automatic classification of web pages based on the concept of domain ontology. Proceeding of the 12th Asia-Pacific Software Engineering Conference (APSEC). Asia-Pacific, 15: 645-651.
   Direct Link
6. Tao, L., M. Sheng and O. Mitsunori, 2004. Document clustering via adaptive subspace iteration. Proceeding of the 27th Annual International ACM Conference on Research and Development in Information Retrieval (SIGIR'04), pp: 218-225.
   Direct Link
7. Wen, L., M. Duoqian and W. Weili, 2011. Two level hierarchical combination method for text classification. Expert Syst. Appl., 38(3): 2030-2039.
   CrossRef    Direct Link
8. Zhang, J.B. and X.M. Liu, 2008. The current situation and application of risk assessment methods on food additives. China Food Addit., 18: 46-51. (In Chinese)
   

Competing interests

The authors have no competing interests.

Open Access Policy

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Copyright

The authors have no competing interests.