Graph Neural Network-Based Forecasting of Nutritional Trends and Consumer Behavior in Health-Oriented Food Markets
Keywords:
Food trend prediction, Graph neural network, Reinforcement learning, BERT, Transformer, Time series model, Market analysisAbstract
This study presents a trend analysis framework based on Graph Neural Networks (GNNs) and Reinforcement Learning (RL), designed to process data from multiple sources, including social media, online shopping records, and nutrition databases. A BERT-GNN model is used to extract food-related topics and sentiment from online text, while a Transformer-based time series model analyzes changes in demand over time. The outputs are combined within an RL structure to support adaptive decision-making under varying market conditions. Compared with conventional approaches such as RNN and ARIMA, the proposed method improves prediction accuracy by 21.7%. The framework also proves effective in detecting rising interest in health-related food products, such as plant-based and probiotic items. These results suggest that the system can serve as a practical tool for anticipating consumption shifts and informing policy or supply chain responses in the food sector.
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