Food Safety Event Detection Based on Multi-Feature Fusion
1
National Engineering Laboratory for Agri-Product Quality Traceability, Beijing Technology and Business University, Beijing 100048, China
2
School of Information, Renmin University of China, Beijing 100872, China
3
School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
*
Author to whom correspondence should be addressed.
Symmetry 2019, 11(10), 1222; https://doi.org/10.3390/sym11101222
Received: 22 August 2019 / Revised: 18 September 2019 / Accepted: 24 September 2019 / Published: 1 October 2019
(This article belongs to the Special Issue Information Technologies and Electronics)
Food safety event detection is a technique used to discover food safety events by monitoring online news. In general, a set of keywords are extracted as features to represent news, and then the news is clustered to generate events. The most popular method for news feature extraction is Term Frequency-Inverse Document Frequency (TF-IDF), however, it has some defects such as being prone to the “dimension disaster”, low computational efficiency, and a lack of semantic information. In addition, Latent Dirichlet Allocation (LDA) is also widely used in news representation. Despite its low dimension, it still suffers from some drawbacks such as the need to set a predefined number of clusters and has difficulty recognizing new events. In this paper, a method based on multi-feature fusion is proposed, which combines the TF-IDF features, the named entity features, and the headline features to represent the news. Based on the representations, the incremental clustering method is used to cluster the news documents and to detect food safety events. Compared with the traditional methods, the proposed method achieved higher Precision, Recall, and F1 scores. The proposed method can help regulatory authorities to make decisions and improve the reputation of the government, whilst reducing social anxiety and economic losses.
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Keywords:
food safety; multi-feature fusion; event detection; TF-IDF
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MDPI and ACS Style
Xiao, K.; Wang, C.; Zhang, Q.; Qian, Z. Food Safety Event Detection Based on Multi-Feature Fusion. Symmetry 2019, 11, 1222.
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