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Article

A Novel Method for Food Market Regulation by Emotional Tendencies Predictions from Food Reviews Based on Blockchain and SAEs

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Beijing Key Laboratory of Big Data Technology for Food Safety, School of Computer, Beijing Technology and Business University, Beijing 100048, China
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Department of Computer and Information Science, University of Macau, Macau 999078, China
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National Engineering Laboratory for Agri-Product Quality Traceability, Beijing Technology and Business University, Beijing 100048, China
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College of Electronic and Information Engineering, Guangdong Ocean University, Zhanjiang 524088, China
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School of Law, China University of Political Science and Law, Beijing 102249, China
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Authors to whom correspondence should be addressed.
Academic Editors: Luís Miguel Cunha and Ana Pinto de Moura
Foods 2021, 10(6), 1398; https://doi.org/10.3390/foods10061398
Received: 7 April 2021 / Revised: 21 May 2021 / Accepted: 27 May 2021 / Published: 17 June 2021
(This article belongs to the Special Issue Individual Determinants of Food Choice in a New Decade)
As a part of food safety research, researches on food transactions safety has attracted increasing attention recently. Food choice is an important factor affecting food transactions safety: It can reflect consumer preferences and provide a basis for market regulation. Therefore, this paper proposes a food market regulation method based on blockchain and a deep learning model: Stacked autoencoders (SAEs). Blockchain is used to ensure the fairness of transactions and achieve transparency within the transaction process, thereby reducing the complexity of the trading environment. In order to enhance the usability, relevant Web pages have been developed to make it more friendly and conduct a security analysis for using blockchain. Consumers’ reviews after the transactions are finished can be used to train SAEs in order to perform emotional tendencies predictions. Compared with different advanced models for predictions, the test results show that SAEs have a better performance. Furthermore, in order to provide a basis for the formulation of regulation strategies and its related policies, case studies of different traders and commodities have also been conducted, proving the effectiveness of the proposed method. View Full-Text
Keywords: blockchain; deep learning; food market; regulation blockchain; deep learning; food market; regulation
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MDPI and ACS Style

Hao, Z.; Wang, G.; Mao, D.; Zhang, B.; Li, H.; Zuo, M.; Zhao, Z.; Yen, J. A Novel Method for Food Market Regulation by Emotional Tendencies Predictions from Food Reviews Based on Blockchain and SAEs. Foods 2021, 10, 1398. https://doi.org/10.3390/foods10061398

AMA Style

Hao Z, Wang G, Mao D, Zhang B, Li H, Zuo M, Zhao Z, Yen J. A Novel Method for Food Market Regulation by Emotional Tendencies Predictions from Food Reviews Based on Blockchain and SAEs. Foods. 2021; 10(6):1398. https://doi.org/10.3390/foods10061398

Chicago/Turabian Style

Hao, Zhihao, Guancheng Wang, Dianhui Mao, Bob Zhang, Haisheng Li, Min Zuo, Zhihua Zhao, and Jerome Yen. 2021. "A Novel Method for Food Market Regulation by Emotional Tendencies Predictions from Food Reviews Based on Blockchain and SAEs" Foods 10, no. 6: 1398. https://doi.org/10.3390/foods10061398

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