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

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.

Keywords:
LSTM; food safety; violations and dangers; group learning