Three-way decisions based feature fusion for Chinese irony detection

Abstract

Irony detection in social networks has drawn much attention in recent years. In this work, we propose a three-way decisions based feature fusion method for Chinese irony detection in microblog. First, we build a set of discriminating features for Chinese irony detection by considering the characteristics of both Chinese language and social networks, including lexical feature, homophonic pun, consecutive punctuation, length of microblog, passive verbs, and affective imbalance. Second, to tackle the problem of extreme imbalance between different kinds of features, we propose a two-stage classification method for Chinese irony detection by considering feature fusion. In the first stage, a three-way decisions based classifier is trained to predict the ironic tendency based on the lexical feature only. This procedure can reduce the thousands-of-dimensions based lexical feature into a one-dimensional feature. In the second stage, the constructed new feature is merged with other five features into a new feature vector for learning. Experimental results show that our proposed two-stage classification method can get a significant improvement than one-stage classification method.