On The Need Of Hierarchical Emotion Classification: Detecting The Implicit Feature Using Constrained Topic Model

Nowadays in China, Sina Weibo has become the most popular microblog platform and researches about it are proposed increasingly. In this paper, the problem of emotion classification of Weibo's posts is addressed in a hierarchical way using a constrained topic model and Support Vector Regression (SVR). Based on this topic model which is variation of Latent Dirichlet Allocation (LDA), an implicit emotion detection algorithm is proposed to identify the underlying emotions. Meanwhile, the constraints are generated based on prior knowledge extraction approaches to compact LDA in order to generate domain-specified topics. Furthermore, a hierarchical emotion structure is employed to classify emotions more precisely into 19 classes. This hierarchy can meet different research granularities. The whole architecture is proposed aimed at alleviating the pain of misclassification caused by feature imbalance and decreasing the labor cost. The experiment results validate that our model outperforms traditional methods with precision, recall and F-scores.