Research on consumers' perception of food risk based on LSTM sentiment classification

Wu Pingzhen,Yuan Shihai,Wu Wenyong

doi:10.1590/fst.47221

Wu Pingzhen, Yuan Shihai + Show 1 more

Open Access

https://doi.org/10.1590/fst.47221

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Abstract

One of the most pressing challenges in people's life is food safety. While many people prefer to purchase meals online since the dawn of the Internet era, regulating food safety online confronts numerous obstacles. A set of food safety evaluation data on violations and dangers was generated by analyzing feedback data from third-party operating systems. A distributed long-term and short-term memory network model was proposed to estimate trader risk values, and a quick warning system for or network attractors was constructed to find the association between opinion data and the amount of online food dangers. Using LSTM-based group learning, this research provides a method for categorizing food safety papers (long-term and short-term memory). First, due to the high cost of human annotation, the food safety document set only comprises one layer of the sample, and food safety document classification based on such a set is a one-layer classification. We propose an automatic body expansion strategy based on a large number of unlabelled web news reports (documents unrelated to food safety) and a binary-based food safety document collection. Select an LSTM-based group learning algorithm for document classification. Food safety documents can be automatically detected from high-performance websites using document classification algorithms based on LSTM-based group learning algorithms.

Highlights

The risk is the probability of negative health consequences and the severity of these effects as a result of the existence of a hazard or hazards in food (Hardy et al, 2018; Membré & Boué, 2018; Zeaki et al, 2019; Zheng et al, 2020a)
HACCP is a food safety management system that was first employed by Pillsbury in 1959 and has subsequently undergone numerous revisions
Unlike a traditional recursive neural network that adds up the input signals and sends them via an activation function, each LSTM unit benefits from one memory calculating memory input (Ct) at a time t. ht or activates LSTM as ht = Γo.tanh(Ct ) where the amount of material supplied through memory is controlled by an output gateway Γo

Summary

Introduction

The risk is the probability of negative health consequences and the severity of these effects as a result of the existence of a hazard or hazards in food (Hardy et al, 2018; Membré & Boué, 2018; Zeaki et al, 2019; Zheng et al, 2020a). The HACCP standard is crucial for adhering to national and international food safety regulations. This standard serves as a risk management tool that complements other food industry management system standards like ISO 22000. HACCP is a food safety system that identifies good production methods and is important for international trade. This standard is notably beneficial to manufacturers, processors, and food service providers. Despite the fact that HACCP is merely a concept and not a standard, it is still the most widely utilized food safety management model in the world. HACCP stands for “Hazard Analysis Critical Control Point,” and it is a food safety management method. HACCP addresses all elements of food safety (Certa et al, 2018; Girardon et al, 2019; Hull-Jackson, 2019; Kang et al, 2018)

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