Study on Forecasting the Social Network Public Opinion of College Employment in Anhui Province
Studying and predicting the social network public opinion on college graduates'employment will help to track and grasp the trend of public opinions on employment,and help the government to introduce precise employment policies.By using Baidu index to construct the text word frequency collection of the public opinion information of the employment social network of colleges and universities in Anhui Province,and building a machine learning EEMD-GRU hybrid model,nonlinear fitting and mapping of social attention of employment rate are carried out.The results show that the EEMD-GRU model can describe and reveal the multi-scale time-frequency characteristics of the social network public opinion of college employment in Anhui province,and the prediction errors of RMSE,MAE and MAPE are only 0.971,0.773 and 0.229,showing high prediction accuracy and stability,indicating that the model can provide a technical research and judgment basis for public opinion of college employment and the formulation of government employment policies.
NETL
NSTL
万方数据
