Grasp the Implicit Features: Hierarchical Emotion Classification based on Topic Model and SVM

Abstract

Microblog post has been a hot research source for emotion classification in recent years. However, due to bloggers' free narrative style and topics' timeliness, the data from microblog post is usually implicit and imbalanced. In this paper, the problems of emotion classification in Chinese microblog posts are solved in a hierarchical way using a knowledge-based topic model and Support Vector Machine(SVM). Based on topic model, an implicit feature detection algorithm is proposed to identify the latent emotions underlying the microblog posts. Meanwhile, a hierarchical emotion structure is employed to classify emotions into 19 classes of four levels by SVM. This structure can meet different research requirements at three granularities. The experiment results validate that our model can achieve better performance in terms of precision, recall and F-scores.