@inproceedings{wu-etal-2017-active,
title = "Active Sentiment Domain Adaptation",
author = "Wu, Fangzhao and
Huang, Yongfeng and
Yan, Jun",
editor = "Barzilay, Regina and
Kan, Min-Yen",
booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P17-1156",
doi = "10.18653/v1/P17-1156",
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.",
}
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%0 Conference Proceedings
%T Active Sentiment Domain Adaptation
%A Wu, Fangzhao
%A Huang, Yongfeng
%A Yan, Jun
%Y Barzilay, Regina
%Y Kan, Min-Yen
%S Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2017
%8 July
%I Association for Computational Linguistics
%C Vancouver, Canada
%F wu-etal-2017-active
%X 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.
%R 10.18653/v1/P17-1156
%U https://aclanthology.org/P17-1156
%U https://doi.org/10.18653/v1/P17-1156
%P 1701-1711
Markdown (Informal)
[Active Sentiment Domain Adaptation](https://aclanthology.org/P17-1156) (Wu et al., ACL 2017)
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.