Research on the Effectiveness of Online Administrative Inquiry Based on Explainable Methods-Taking the"Please Speak Up"Online Administrative Inquiry Platform as an Example
With the accelerated advancement of digital government construction,online administrative inquiry plays an indispensable role in social governance of China.In order to explore the key factors affecting the effectiveness of online administrative inquiry,this study focuses on the relevant data from the Luzhou online administrative inquiry platform"Please Speak Up".This study adopts a text data mining method combining various machine learning and deep learning models to identify characteris-tic variables in online administrative inquiry texts,construct two public satisfaction classification models.And multiple explainable methods are used to explain the model results from both structural and semantic features.The research finds that variables such as administrative inquiry sentiment,length of administrative inquiry text,type of appeal,response sentiment,type of response agency,length of response time all have varying degrees of influence on public satisfaction.In addition,the explainable framework constructed by this study can also effectively identify key content in online administrative inquiry,such as time,location,and organization names.
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