IFoodCloud: A Platform for Real-time Sentiment Analysis of Public Opinion about Food Safety in China
This provides a tool for food safety supervision and risk communication in China, but it is incremental as it applies existing sentiment analysis methods to a new domain-specific dataset.
The researchers tackled the problem of analyzing public opinion on food safety in China by developing IFoodCloud, a platform for real-time sentiment analysis, which achieved an F1-score of 0.9737 with their best model and demonstrated application through three real-world cases.
The Internet contains a wealth of public opinion on food safety, including views on food adulteration, food-borne diseases, agricultural pollution, irregular food distribution, and food production issues. In order to systematically collect and analyse public opinion on food safety, we developed IFoodCloud, a platform for the real-time sentiment analysis of public opinion on food safety in China. It collects data from more than 3,100 public sources that can be used to explore public opinion trends, public sentiment, and regional attention differences of food safety incidents. At the same time, we constructed a sentiment classification model using multiple lexicon-based and deep learning-based algorithms integrated with IFoodCloud that provide an unprecedented rapid means of understanding the public sentiment toward specific food safety incidents. Our best model's F1-score achieved 0.9737. Further, three real-world cases are presented to demonstrate the application and robustness. IFoodCloud could be considered a valuable tool for promote scientisation of food safety supervision and risk communication.