Affiliations: [a] State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology, Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China | [b] Shijiazhuang Preschool Teachers College, Shijiazhuang 050228, Hebei, China
Correspondence:
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Corresponding author: Hua Xu, State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology, Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China. Tel.: +86 1062796450; Fax: +86 1062771792; E-mail: [email protected].
Abstract: 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.
Keywords: Text mining, emotion classification, microblog, topic model
