Joint Emotion-Topic Modeling for Social Affective Text Mining

被引：49
|
作者：
Bao, Shenghua [1 ,2 ]
Xu, Shengliang
Zhang, Li [1 ]
Yan, Rong [3 ]
Su, Zhong [1 ]
Han, Dingyi [2 ]
Yu, Yong [2 ]
机构：
[1] IBM Res China, Beijing 100193, Peoples R China
[2] Shanghai Jiao Tong Univ, Shanghai 200240, Peoples R China
[3] IBM Corp, TJ Watson Res Ctr, Hawthorne, NY 10523 USA
来源：
2009 9TH IEEE INTERNATIONAL CONFERENCE ON DATA MINING | 2009年
关键词：
D O I：
10.1109/ICDM.2009.94
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper is concerned with the problem of social affective text mining, which aims to discover the connections between social emotions and affective terms based on user-generated emotion labels. We propose a joint emotion-topic model by augmenting latent Dirichlet allocation with an additional layer for emotion modeling. It first generates a set of latent topics from emotions, followed by generating affective terms from each topic. Experimental results on an online news collection show that the proposed model can effectively identify, meaningful latent topics for each emotion. Evaluation on emotion prediction further verifies the effectiveness of the proposed model.
引用
收藏
页码：699 / +
页数：2
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