Emotion Classification in Microblog Texts Using Class Sequential Rules

This paper studies the problem of emotion classification in microblog texts. Given a microblog text which consists of several sentences, we classify its emotion as anger, disgust, fear, happiness, like, sadness or surprise if available. Existing methods can be categorized as lexicon based methods or machine learning based methods. However, due to some intrinsic characteristics of the microblog texts, previous studies using these methods always get unsatisfactory results. This paper introduces a novel approach based on class sequential rules for emotion classification of microblog texts. The approach first obtains two potential emotion labels for each sentence in a microblog text by using an emotion lexicon and a machine learning approach respectively, and regards each microblog text as a data sequence. It then mines class sequential rules from the dataset and finally derives new features from the mined rules for emotion classification of microblog texts. Experimental results on a Chinese benchmark dataset show the superior performance of the proposed approach.