Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
11-2018
Abstract
In this paper, we target at improving the performance of multi-label emotion classification with the help of sentiment classification. Specifically, we propose a new transfer learning architecture to divide the sentence representation into two different feature spaces, which are expected to respectively capture the general sentiment words and the other important emotion-specific words via a dual attention mechanism. Extensive experimental results demonstrate that our transfer learning approach can outperform several strong baselines and achieve the state-of-the-art performance on two benchmark datasets.
Discipline
Databases and Information Systems | Social Media
Research Areas
Data Science and Engineering
Publication
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: Brussels, Belgium, October 31 - November 4
First Page
1097
Last Page
1102
Publisher
ACL
City or Country
New Brunswick, NJ
Citation
YU, Jianfei; MARUJO, Luis; JIANG, Jing; KARUTURI, Pradeep; and BRENDEL, William.
Improving multi-label emotion classification via sentiment classification with dual attention transfer network. (2018). Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: Brussels, Belgium, October 31 - November 4. 1097-1102.
Available at: https://ink.library.smu.edu.sg/sis_research/4279
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.