A Study on the Factors Influencing Public Satisfaction with Government-Citizen Interaction Platforms:An Analysis Based on Leadership Mailbox Corpus
[Objective]This study investigates the factors influencing public satisfaction with government-citizen interaction platforms.We constructed an analysis model for factors affecting public satisfaction.[Methods]We extracted micro-level variables from the leadership mailbox corpus,which were combined with macroeconomic variables to establish a public satisfaction analysis model using the Gradient Boosting Decision Tree(GBDT)method.We also eliminated less influential variables with SHAP analysis to optimize the model.[Results]The proposed model outperformed comparison models across accuracy,recall,precision,and F1-score.Key features affecting public satisfaction with the leadership mailbox include GDP growth rate,PCDI growth rate,CPI growth rate,message topic,message type,and response mode.[Limitations]The study did not explore a broader range of influencing factors or more extensive government-citizen interaction scenarios.[Conclusions]The new model optimizes the variable selection process and visualizes how each feature influences the level,direction,and manner of public satisfaction with government responses.The model is a data-driven tool for administrative decision-making.
Public SatisfactionGovernment-Citizen InteractionMachine LearningLeader's Mailbox
NETL
NSTL
万方数据
