Modular Policy Evaluation System: A Policy Evaluation Framework Based on Text Mining

Nowadays, in the big data era, data processing technology facilitates us to get the utmost out of sufficient information in the data. However, few scholars apply big data technology to the field of policy evaluation. Therefore, under the Policy Modeling Consistency(PMC) index model framework, this paper thoroughly mines the valuable information in policy texts and proposes a modular policy evaluation system combining text mining and machine learning methods. The system is divided into four processing modules, including data acquisition, data processing, index evaluation construction, and score evaluation. Compared with the traditional policy evaluation methods, the modular policy evaluation system presents the advantages of objectivity, high accuracy, and high efficiency, assisting in government policies' implementation.