[Purpose/significance] It is of great value to grasp the concept of government behavior and governance ac⁃curately, based on the dictionary method to construct a fine grain analysis of Chinese policy texts suitable for the open data environment. [Method/process] The words that reflect emotional intensity in policy texts are defined as tendency words, and their structural characteristics and semantic relevance are used to construct a tendency dictionary. First, the seed words are extracted according to the interpretation opinions of domain experts and combined with the point mutual information algorithm to expand the dictionary online. Secondly, based on the theory of formal concept analysis, the top⁃ic connotation of policy texts is defined and quantified, the hierarchical relationship between policy topics is mapped to the semantic relationship between words, and the synonymous tendency words with topic similarity are screened. Final⁃ly, the credibility and validity methods are used for empirical testing. [Result/conclusion] The propensity dictionary
has high accuracy and recall rate in the task of policy text emotion recognition, which is suitable for large-scale fine grain policy text analysis, and provides a reliable and novel quantitative tool for policy research and decision-making.