Ensemble of SVM Classifiers with Different Representations for Societal Risk Classification

Abstract

Using the posts of Tianya Forum as the data source and adopting the societal risk indicators from socio psychology, we conduct document-level multiple societal risk classification of BBS posts. Two kinds of models are applied to generate the representations of posts respectively: Bag-of-Words focuses on extracting the occurrence information of words in posts, and a deep learning model as Post Vector is designed to capture the semantics and word order of posts. Based on the different post representations, two types of support vector machine (SVM) classifiers are developed and compared in the societal risk classification of the posts. Furthermore, as the complementary information contained in the two different post representations, several SVM ensemble methods at the decision score level of the two SVM classifiers are proposed to improve the performance of societal risk classification. The experimental results reveal that the SVM ensemble method achieves better results in document-level societal risk classification than SVM based on single representation.