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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 YuanAuthors Info & Claims

SIGIR '12: Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval

Pages 1059 - 1060

https://doi.org/10.1145/2348283.2348468

Published: 12 August 2012 Publication History

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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.

References

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D. Das and S. Bandyopadhyay. Sentence to document level emotion tagging--a coarse-grained study on bengali blogs. Advances in Pattern Recognition, 2010.

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K. Lin, C. Yang, and H. Chen. What emotions do news articles trigger in their readers? In SIGIR, pages 733--734, 2007.

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Cited By

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Wang CZhang YJie WSauer CYuan X(2017)Hierarchical Semantic Representations of Online News Comments for Emotion Tagging Using Multiple Information SourcesDatabase Systems for Advanced Applications10.1007/978-3-319-55699-4_8(121-136)Online publication date: 22-Mar-2017
https://doi.org/10.1007/978-3-319-55699-4_8
Yu LZhao XWang CZhang HZhang Y(2016)Cross-Collection Emotion Tagging for Online NewsWeb-Age Information Management10.1007/978-3-319-39958-4_18(225-237)Online publication date: 2-Jun-2016
https://doi.org/10.1007/978-3-319-39958-4_18
Quan XWang QZhang YSi LWenyin L(2015)Latent Discriminative Models for Social Emotion Detection with Emotional DependencyACM Transactions on Information Systems10.1145/274945934:1(1-19)Online publication date: 28-Jul-2015
https://dl.acm.org/doi/10.1145/2749459
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Index Terms

Emotion tagging for comments of online news by meta classification with heterogeneous information sources
1. Information systems
  1. Information retrieval

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Information

Published In

SIGIR '12: Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval

August 2012

1236 pages

ISBN:9781450314725

DOI:10.1145/2348283

General Chair:
William Hersh
Oregon Health & Science University, USA
,
Program Chairs:
Jamie Callan
Carnegie Mellon University, USA
,
Yoelle Maarek
Yahoo! Research, Israel
,
Mark Sanderson
Royal Melbourne Institute of Technology, Australia

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Association for Computing Machinery

New York, NY, United States

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Published: 12 August 2012

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SIGIR '12: The 35th International ACM SIGIR conference on research and development in Information Retrieval

August 12 - 16, 2012

Oregon, Portland, USA

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Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Wang CZhang YJie WSauer CYuan X(2017)Hierarchical Semantic Representations of Online News Comments for Emotion Tagging Using Multiple Information SourcesDatabase Systems for Advanced Applications10.1007/978-3-319-55699-4_8(121-136)Online publication date: 22-Mar-2017
https://doi.org/10.1007/978-3-319-55699-4_8
Yu LZhao XWang CZhang HZhang Y(2016)Cross-Collection Emotion Tagging for Online NewsWeb-Age Information Management10.1007/978-3-319-39958-4_18(225-237)Online publication date: 2-Jun-2016
https://doi.org/10.1007/978-3-319-39958-4_18
Quan XWang QZhang YSi LWenyin L(2015)Latent Discriminative Models for Social Emotion Detection with Emotional DependencyACM Transactions on Information Systems10.1145/274945934:1(1-19)Online publication date: 28-Jul-2015
https://dl.acm.org/doi/10.1145/2749459
Yu LZhifan YNie PZhao XZhang Y(2015)Multi-source Emotion Tagging for Online NewsProceedings of the 2015 12th Web Information System and Application Conference (WISA)10.1109/WISA.2015.24(49-52)Online publication date: 11-Sep-2015
https://dl.acm.org/doi/10.1109/WISA.2015.24
Zhang YSu LYang ZZhao XYuan X(2015)Multi-Label Emotion Tagging for Online News by Supervised Topic ModelWeb Technologies and Applications10.1007/978-3-319-25255-1_6(67-79)Online publication date: 13-Nov-2015
https://doi.org/10.1007/978-3-319-25255-1_6
Zhang YZhang NSi LLu YWang QYuan XGeva STrotman ABruza PClarke CJärvelin K(2014)Cross-domain and cross-category emotion tagging for comments of online newsProceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval10.1145/2600428.2609587(627-636)Online publication date: 3-Jul-2014
https://dl.acm.org/doi/10.1145/2600428.2609587

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Cross-domain and cross-category emotion tagging for comments of online news

Nasty online comments anger you more than me, but nice ones make me as happy as you

Weighted Domain Translation for Online News Comments Emotion Tagging

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