Assessing the Influence Level of Food Safety Public Opinion with Unbalanced Samples Using Ensemble Machine Learning

Abstract

Assessing the public opinion on food safety events constitutes an important job of government regulators. To optimize the government’s management of food safety affairs, a promising way is to use artificial intelligence to improve the efficiency of food safety public opinion assessment. In this paper, we model the assessment of public opinion influence as a text classification task. The whole model adopts the ensemble learning framework, and it integrates naive Bayes, support vector machine, extreme gradient boosting, convolutional neural network, long- and short-term memory network, FastText, and BERT classification methods into the framework to form an ensemble learner. The ensemble learner is able to classify textual public opinion into high, medium, and low influence levels by learning from the samples assessed by human experts. To overcome the problem of unbalanced samples, we propose a sample generation method consisting of synonym replacement and semantic filtering to increase the number of high-influence samples. Real public opinion data collected from the Food Safety Department of the Chinese government are used for experiment. Extensive comparison of the proposed method with baseline methods proves the effectiveness of the ensemble learner and the sample generation steps.