@InProceedings{dou:2017:EMNLP2017,
  author    = {Dou, Zi-Yi},
  title     = {Capturing User and Product Information for Document Level Sentiment Analysis with Deep Memory Network},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {521--526},
  abstract  = {Document-level sentiment classification is a fundamental problem which aims to
	predict a user's overall sentiment about a 
	product in a document. Several methods have been proposed to tackle the problem
	whereas most of them fail to consider the influence of users who express the
	sentiment and products which are evaluated. To address the issue,
	we propose a deep memory network for document-level sentiment classification
	which could capture the user and product information at the same time. To prove
	the effectiveness of our algorithm, we conduct experiments on IMDB and Yelp
	datasets and the results indicate that our model can achieve better performance
	than several existing methods.},
  url       = {https://www.aclweb.org/anthology/D17-1054}
}