Emotion tagging for comments of online news by meta classification with heterogeneous information sources

Authors:
Ying Zhang;Yi Fang;Xiaojun Quan;Lin Dai;Luo Si;Xiaojie Yuan
Affiliations:
Nankai University, Tianjin, China;Purdue University, West Lafayette, IN, USA;City University of Hong Kong, Hong Kong, China;Beijing Institute of Technology, Beijing, China;Purdue University, West Lafayette, IN, USA;Nankai University, Tianjin, China
Venue:
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Year:
2012

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Abstract

With the rapid growth of online news services, users can actively respond to online news by making comments. Users often express subjective emotions in comments such as sadness, surprise and anger. Such emotions can help understand the preferences and perspectives of individual users, and therefore may facilitate online publishers to provide users with more relevant services. This paper tackles the task of predicting emotions for the comments of online news. To the best of our knowledge, this is the first research work for addressing the task. In particular, this paper proposes a novel Meta classification approach that exploits heterogeneous information sources such as the content of the comments and the emotion tags of news articles generated by users. The experiments on two datasets from online news services demonstrate the effectiveness of the proposed approach.