Comprehensive commodity price forecasting framework using text mining methods

Abstract Exploiting advanced and appropriate methods to construct high‐quality features from different types of data becomes crucial in agricultural futures price forecasting. Thus, this study develops a comprehensive commodity price forecasting framework using text mining methods. First, the modal features of the price series are extracted using the proposed Integrated‐EEMD‐VMD‐SE method, and dynamic topic sentiment features are constructed from Weibo texts using the proposed dynamic topic model joint sentiment analysis method. Second, combined with statistical variables, lag order selection and feature selection are performed on these comprehensive factors. Finally, 12 comparative prediction models are designed based on random forest (RF), long short‐term memory (LSTM), and multilayer perceptron (MLP), and empirical analysis is carried out on two cases of pork prices and soybean futures prices. The experimental results show that the proposed prediction framework has high prediction accuracy, and the mean absolute percentage error (MAPE) values are 1.00 and 0.92, respectively. The constructed time series modal features and dynamic topic sentiment features can significantly improve the performance of the prediction model.