Weighted cluster-level social emotion classification across domains

Fu Lee WANG, Zhengwei ZHAO, Gary CHENG*, Yanghui RAO, Haoran XIE

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

Abstract

Social emotion classification is important for better capturing the preferences and perspectives of individual users to monitor public opinion and edit news. However, news reports have a strong domain dependence. Moreover, training data in the target domain are usually insufficient and only a small amount of training data may be labeled. To address these problems, we develop a cluster-level method for social emotion classification across domains. By discovering both source and target clusters and weighting the cluster in the source domain according to the similarity between its distribution and that of the target cluster, we can discover common patterns between the source and target domains, thus using both source and target data more effectively. Extensive experiments involving 12 cross-domain tasks conducted by using the ChinaNews dataset show that our model outperforms existing methods.
Original languageEnglish
JournalInternational Journal of Machine Learning and Cybernetics
DOIs
Publication statusE-pub ahead of print - 5 Jan 2023

Bibliographical note

Funding Information:
The research described in this article has been supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China under Grant UGC/FDS16/E01/19; Lam Woo Research Fund (LWP20019), and the Faculty Research Grants (DB22B4 and DB23A3) of Lingnan University, Hong Kong.

Publisher Copyright:
© 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

Keywords

  • Cross domain
  • Document clustering
  • Emotion classification

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