Exploring effective methods for on-line societal risk classification and feature mining

Abstract

China has to face lots of societal conflicts during periods of social and economic transformation. It is crucial to exactly detect societal risk for the mission to a harmonious society. On-line community concerns have been mapped into respective societal risks and support vector machine model has been used for risk multi-classification on Baidu hot news search words (HNSW). Different from traditional text classification, societal risk classification is a more complicated issue which relates to socio-psychology. Conditional random fields (CRFs) model is applied to access to societal risk perception more accurately. We regard the risks of all the terms throughout a hot search word as a sequential flow of risks. The experimental results show that CRFs model has superior performance with capturing the contextual constraints on HNSW. Besides, state features can be extracted based on CRFs model to study distributions of terms in each risk category. The distribution rules of geographical terms are found and summarized.