Multimodal Online Public Opinion Event Extraction and Trend Prediction for Edible Agricultural Products

With the advent of the information age, food safety issues often trigger public panic and even disrupt social order. Traditional public opinion analysis methods struggle to handle massive amounts of data and effectively predict public sentiment trends. To address this problem, this paper proposes a deep reinforcement learning-based method for predicting public opinion trends related to food safety. First, a multimodal event extraction method is designed to extract image information and fuse it with text to generate new textual information. Then, an event detection model is designed based on the HDBSCAN clustering algorithm to more accurately identify emerging issues in safety-sensitive areas. Finally, this paper proposes a deep reinforcement learning-based model to predict public sentiment trends regarding food safety. Experimental results show that, compared with traditional methods, the proposed method has higher accuracy and adaptability in handling sudden or delayed public opinion events.