Analysis and Recognition of Food Safety Problems in Online Ordering Based on Reviews Text Mining

Abstract

In the era of big data, the online ordering form of “Internet + traditional catering” has adapted to the needs of consumers with a fast pace of life and personalized consumption mode and is booming all over the world. However, due to the consumer information asymmetry and the lack of effective supervision, the potential food safety problems are becoming increasingly prominent. This paper comprehensively uses the social network analysis and Latent Dirichlet Allocation method to mine the text data of consumer comments on the online ordering platform and puts forward five food safety problems existing in the online ordering platform. Then, text features are extracted by using Bert, TF-IDF, Word2vec, and N-gram algorithms, and classifiers based on GBDT, XGBoost, LSTM, BiLSTM, CNN, RNN, and CRNN algorithms are cross constructed to identify text reviews with potential food safety hazards. The classifier’s performance is compared and evaluated through ten-fold cross-validation, Friedman test, and confusion matrix. The research results show that the BERT-GBDT classifier has the best performance in accuracy, precision, specificity, and F1 measure value, and stability is the strongest. It has the best distinguish effect on the text of the review with potential food safety hazards.

Innovative Label Embedding for Food Safety Comment Classification: Fusion of Self-Semantic and Self-Knowledge Features

“…This enables the timely resolution of food safety issues. Nevertheless, faced with the substantial volume of daily-generated food safety information, the key challenge lies in accurately classifying this information according to specific rules and assigning suitable labels [6,7].…”