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Erschienen in:
2015 | OriginalPaper | Buchkapitel
Detection of Food Safety Topics Based on SPLDAs
verfasst von : Jinshuo Liu, Yabo Li, Yingyue Peng, Juan Deng, Xin Chen
Erschienen in: International Conference on Security and Privacy in Communication Networks
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Abstract
Nowadays, the problems of food safety are more and more serious. This paper focuses
on network topic detection of food safety problems, which is difficult because of several reasons, such
as various description of a same problem and sparseness of the data. In this paper, a novel method based
on Single-pass in LDA space is proposed to detect the food safety problems from various sources, such
as microblog and news reports. The experiments show that the method could detect food safety topics efficiently.
The F-measure value of clustering almost increases from 56.03 % to 87.21 %, compared with Single-Pass
based on traditional VSM. In addition, experiments about the influence of similarity parameter to models’
performance demonstrate that our method has a better robustness.