Research Article
Detection of Food Safety Topics Based on SPLDAs
@INPROCEEDINGS{10.1007/978-3-319-23829-6_42, author={Jinshuo Liu and Yabo Li and Yingyue Peng and Juan Deng and Xin Chen}, title={Detection of Food Safety Topics Based on SPLDAs}, proceedings={International Conference on Security and Privacy in Communication Networks. 10th International ICST Conference, SecureComm 2014, Beijing, China, September 24-26, 2014, Revised Selected Papers, Part I}, proceedings_a={SECURECOMM}, year={2015}, month={11}, keywords={Food safety Topic detection LDA space Single-Pass}, doi={10.1007/978-3-319-23829-6_42} }- Jinshuo Liu
Yabo Li
Yingyue Peng
Juan Deng
Xin Chen
Year: 2015
Detection of Food Safety Topics Based on SPLDAs
SECURECOMM
Springer
DOI: 10.1007/978-3-319-23829-6_42
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.
