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.
Highlights
Nowadays, people are used to expressing opinions on the Internet, which leads to an explosive growth in the amount of online public opinions
It has been shown that the interaction of government agencies with public opinions through social media can help the government to respond to public events efficiently [1]. e government can use public opinion assessment to explore people’s attitudes towards an event [2,3,4] and predict events that may lead to serious consequences [5]
Each base learner has its corresponding text preprocessing step: for naive Bayes (NB), support vector machine (SVM), and XGBoost, each public opinion sample is turned into a vector of TF-IDF weights, together with the influence level label of this sample; for convolutional neural network (CNN) and long- and short-term memory network (LSTM), each public opinion sample is turned into a matrix whose columns correspond to the embedding of words; for FastText, it takes the original text as input; for BERT, as it limits the length of input text for efficiency consideration, we apply automatic summarization to shorten oversized public opinion samples
Summary
People are used to expressing opinions on the Internet, which leads to an explosive growth in the amount of online public opinions. E government can use public opinion assessment to explore people’s attitudes towards an event [2,3,4] and predict events that may lead to serious consequences [5]. Erefore, it is meaningful to assess the influence of food safety public opinion in the early stage of its formation. E importance of food safety public opinion has been pointed out in various regulatory documents issued by the government [6, 7]. Unlike many other management optimization fields which have been intensively studied [8, 9], currently there is not much research dedicated to food safety public opinion assessment. Since we formalize the public opinion assessment problem as a text classification problem, the literature of text classification is analyzed to show the character of our research
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