@InProceedings{wu-huang-yan:2017:Long,
  author    = {Wu, Fangzhao  and  Huang, Yongfeng  and  Yan, Jun},
  title     = {Active Sentiment Domain Adaptation},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  month     = {July},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {1701--1711},
  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.},
  url       = {http://aclweb.org/anthology/P17-1156}
}