Sentiment Domain Adaptation with Multiple Sources

Fangzhao Wu, Yongfeng Huang
2016 Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)  
Domain adaptation is an important research topic in sentiment analysis area. Existing domain adaptation methods usually transfer sentiment knowledge from only one source domain to target domain. In this paper, we propose a new domain adaptation approach which can exploit sentiment knowledge from multiple source domains. We first extract both global and domain-specific sentiment knowledge from the data of multiple source domains using multi-task learning. Then we transfer them to target domain
more » ... th the help of words' sentiment polarity relations extracted from the unlabeled target domain data. The similarities between target domain and different source domains are also incorporated into the adaptation process. Experimental results on benchmark dataset show the effectiveness of our approach in improving cross-domain sentiment classification performance.
doi:10.18653/v1/p16-1029 dblp:conf/acl/WuH16 fatcat:pvp3dtw7fjby5arqbde7ktntby