| Title | Policy Text Analysis Based on Text Mining and Fuzzy Cognitive Map |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Han, H., Wang, Q., Chen, C. |
| Conference Name | 2019 15th International Conference on Computational Intelligence and Security (CIS) |
| Date Published | dec |
| Keywords | causal relationships, data mining, Data models, FARM, fuzzy association rule mining, fuzzy cognitive map, Fuzzy cognitive maps, fuzzy set theory, latent semantic analysis, Layout, natural language processing, partial association test, policy documents, policy elements, policy text analysis, policy-based governance, pubcrawl, Security Policies Analysis, Semantics, soft computing method, state owned capital layout adjustment, text analysis, text mining, time division multiplexing |
| Abstract | With the introduction of computer methods, the amount of material and processing accuracy of policy text analysis have been greatly improved. In this paper, Text mining(TM) and latent semantic analysis(LSA) were used to collect policy documents and extract policy elements from them. Fuzzy association rule mining(FARM) technique and partial association test (PA) were used to discover the causal relationships and impact degrees between elements, and a fuzzy cognitive map (FCM) was developed to deduct the evolution of elements through a soft computing method. This non-interventionist approach avoids the validity defects caused by the subjective bias of researchers and provides policy makers with more objective policy suggestions from a neutral perspective. To illustrate the accuracy of this method, this study experimented by taking the state-owned capital layout adjustment related policies as an example, and proved that this method can effectively analyze policy text. |
| DOI | 10.1109/CIS.2019.00038 |
| Citation Key | han_policy_2019 |
Policy Text Analysis Based on Text Mining and Fuzzy Cognitive Map