  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.
