Active Sentiment Domain Adaptation

Fangzhao Wu, Yongfeng Huang, Jun Yan


Abstract
Domain adaptation is an important technology to handle domain dependence problem in sentiment analysis field. Existing methods usually rely on sentiment classifiers trained in source domains. However, their performance may heavily decline if the distributions of sentiment features in source and target domains have significant difference. In this paper, we propose an active sentiment domain adaptation approach to handle this problem. Instead of the source domain sentiment classifiers, our approach adapts the general-purpose sentiment lexicons to target domain with the help of a small number of labeled samples which are selected and annotated in an active learning mode, as well as the domain-specific sentiment similarities among words mined from unlabeled samples of target domain. A unified model is proposed to fuse different types of sentiment information and train sentiment classifier for target domain. Extensive experiments on benchmark datasets show that our approach can train accurate sentiment classifier with less labeled samples.
Anthology ID:
P17-1156
Volume:
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2017
Address:
Vancouver, Canada
Editors:
Regina Barzilay, Min-Yen Kan
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1701–1711
Language:
URL:
https://aclanthology.org/P17-1156
DOI:
10.18653/v1/P17-1156
Bibkey:
Cite (ACL):
Fangzhao Wu, Yongfeng Huang, and Jun Yan. 2017. Active Sentiment Domain Adaptation. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1701–1711, Vancouver, Canada. Association for Computational Linguistics.
Cite (Informal):
Active Sentiment Domain Adaptation (Wu et al., ACL 2017)
Copy Citation:
PDF:
https://aclanthology.org/P17-1156.pdf