Issue No. 09 - Sept. (2012 vol. 24)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TKDE.2011.188
Shenghua Bao , IBM Research-China, Beijing
Shengliang Xu , Shanghai Jiao Tong University, Shanghai
Li Zhang , IBM Research-China, Beijing
Rong Yan , Facebook, Palo Alto
Zhong Su , IBM China-Research, Beijing
Dingyi Han , Shanghai Jiao Tong University, Shanghai
Yong Yu , Shanghai Jiao Tong University, Shanghai
This paper is concerned with the problem of mining social emotions from text. Recently, with the fast development of web 2.0, more and more documents are assigned by social users with emotion labels such as happiness, sadness, and surprise. Such emotions can provide a new aspect for document categorization, and therefore help online users to select related documents based on their emotional preferences. Useful as it is, the ratio with manual emotion labels is still very tiny comparing to the huge amount of web/enterprise documents. In this paper, we aim to discover the connections between social emotions and affective terms and based on which predict the social emotion from text content automatically. More specifically, 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.
Text mining, Predictive models, Joints, Context modeling, Data models, Blogs, Software, performance evaluation, Affective text mining, emotion-topic model
Z. Su et al., "Mining Social Emotions from Affective Text," in IEEE Transactions on Knowledge & Data Engineering, vol. 24, no. , pp. 1658-1670, 2011.