@inproceedings{54d7b3b3d2c44801aaba0f0ea72afb9c,
title = "Emotion tagging for comments of online news by meta classification with heterogeneous information sources",
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.",
keywords = "emotion tagging, meta classification, online news",
author = "Ying Zhang and Yi Fang and Xiaojun Quan and Lin Dai and Luo Si and Xiaojie Yuan",
year = "2012",
doi = "10.1145/2348283.2348468",
language = "English",
isbn = "9781450316583",
series = "SIGIR'12 - Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval",
pages = "1059--1060",
booktitle = "SIGIR'12 - Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval",
note = "35th Annual ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2012 ; Conference date: 12-08-2012 Through 16-08-2012",
}