skip to main content
10.1145/3132847.3133063acmconferencesArticle/Chapter ViewAbstractPublication PagescikmConference Proceedingsconference-collections
short-paper

Cluster-level Emotion Pattern Matching for Cross-Domain Social Emotion Classification

Published: 06 November 2017 Publication History

Abstract

This paper addresses the task of cross-domain social emotion classification of online documents. The cross-domain task is formulated as using abundant labeled documents from a source domain and a small amount of labeled documents from a target domain, to predict the emotion of unlabeled documents in the target domain. Although several cross-domain emotion classification algorithms have been proposed, they require that feature distributions of different domains share a sufficient overlapping, which is hard to meet in practical applications. This paper proposes a novel framework, which uses the emotion distribution of training documents at the cluster level, to alleviate the aforementioned issue. Experimental results on two datasets show the effectiveness of our proposed model on cross-domain social emotion classification.

References

[1]
A. Acharya, E. R. Hruschka, J. Ghosh, and S. Acharyya. 2014. An optimization framework for combining ensembles of classifiers and clusterers with applications to nontransductive semisupervised learning and transfer learning. ACM Transactions on Knowledge Discovery from Data, Vol. 9, 1 (2014), 1--35.
[2]
S. Bao, S. Xu, L. Zhang, R. Yan, Z. Su, D. Han, and Y. Yu. 2012. Mining social emotions from affective text. IEEE Transactions on Knowledge and Data Engineering, Vol. 24, 9 (2012), 1658--1670.
[3]
J. Blitzer, M. Dredze, F. Pereira, et almbox. 2007. Biographies, bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification ACL. 440--447.
[4]
J. Blitzer, R. McDonald, and F. Pereira. 2006. Domain adaptation with structural correspondence learning EMNLP. 120--128.
[5]
W. De Smet and M. F. Moens. 2009. An aspect based document representation for event clustering CLIN.
[6]
X. Li, Y. Rao, Y. Chen, X. Liu, and H. Huang. 2016. Social emotion classification via reader perspective weighted model AAAI. 4230--4231.
[7]
B. Pang, L. Lee, et almbox. 2008. Opinion mining and sentiment analysis. Foundations and Trends® in Information Retrieval, Vol. 2, 1--2 (2008), 1--135.
[8]
Y. Rao. 2016. Contextual sentiment topic model for adaptive social emotion classification. IEEE Intelligent Systems Vol. 31, 1 (2016), 41--47.
[9]
C. Strapparava and R. Mihalcea. 2007. Semeval-2007 task 14: Affective text. In SemEval. 70--74.
[10]
Y. Zhang, N. Zhang, L. Si, Y. Lu, Q. Wang, and X. Yuan. 2014. Cross-domain and cross-category emotion tagging for comments of online news SIGIR. 627--636.

Cited By

View all
  • (2023)Semi-Supervised Sentiment Classification and Emotion Distribution Learning Across DomainsACM Transactions on Knowledge Discovery from Data10.1145/357173617:5(1-30)Online publication date: 27-Feb-2023
  • (2023)Weighted cluster-level social emotion classification across domainsInternational Journal of Machine Learning and Cybernetics10.1007/s13042-022-01769-314:7(2385-2394)Online publication date: 5-Jan-2023
  • (2019)Character-Aware Convolutional Recurrent Networks with Self-Attention for Emotion Detection on Twitter2019 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN.2019.8852171(1-8)Online publication date: Jul-2019

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
CIKM '17: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management
November 2017
2604 pages
ISBN:9781450349185
DOI:10.1145/3132847
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 November 2017

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. clustering
  2. cross-domain classification
  3. emotion detection

Qualifiers

  • Short-paper

Funding Sources

  • Research Grants Council of Hong Kong Special Administrative Region, China
  • the National Natural Science Foundation of China
  • the Internal Research Grant of The Education University of Hong Kong

Conference

CIKM '17
Sponsor:

Acceptance Rates

CIKM '17 Paper Acceptance Rate 171 of 855 submissions, 20%;
Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

Upcoming Conference

CIKM '25

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)2
  • Downloads (Last 6 weeks)0
Reflects downloads up to 20 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2023)Semi-Supervised Sentiment Classification and Emotion Distribution Learning Across DomainsACM Transactions on Knowledge Discovery from Data10.1145/357173617:5(1-30)Online publication date: 27-Feb-2023
  • (2023)Weighted cluster-level social emotion classification across domainsInternational Journal of Machine Learning and Cybernetics10.1007/s13042-022-01769-314:7(2385-2394)Online publication date: 5-Jan-2023
  • (2019)Character-Aware Convolutional Recurrent Networks with Self-Attention for Emotion Detection on Twitter2019 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN.2019.8852171(1-8)Online publication date: Jul-2019

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media