Abstract The complex relevancy between public opinion in multiple related fields is exploited to deal with the lack of public opinion consistency caused by the discontinuity of news in specific fields, so that it can be used for price index prediction of sub items. This research proposes a public opinion index network method based on self-learning graph neural network model, which takes public opinion indexes in several related specific fields as network nodes, and daily public opinion quantitative values are regarded as time series. By learning the structural characteristics of hidden graphs and the temporal characteristics of public opinion data, a dynamic public opinion index network is constructed to fill the sparse public opinion values. The empirical research on the prediction of food price index and non-ferrous metal production price index shows that the correlation between the public opinion index supplemented by this method and the corresponding price statistics index reaches the highest, and the prediction accuracy is improved, which verifies the effectiveness of this method.
CAO Lei,SHANG Wei,XIE Shiyao等. Research on Price Index Prediction Based on AGNN Public Opinion Index Network[J]. Chinese Journal of Management, 2023, 20(3): 411-.
