@InProceedings{ma-EtAl:2017:I17-1,
  author    = {Ma, Dehong  and  Li, Sujian  and  Zhang, Xiaodong  and  Wang, Houfeng  and  Sun, Xu},
  title     = {Cascading Multiway Attentions for Document-level Sentiment Classification},
  booktitle = {Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)},
  month     = {November},
  year      = {2017},
  address   = {Taipei, Taiwan},
  publisher = {Asian Federation of Natural Language Processing},
  pages     = {634--643},
  abstract  = {Document-level sentiment classification aims to assign the user reviews a
	sentiment polarity. Previous methods either just utilized the document content
	without consideration of user and product information, or did not
	comprehensively consider what roles the three kinds of information play in text
	modeling. In this paper, to reasonably use all the information, we present the
	idea that  user, product and their combination can all influence the generation
	of attentions to words and sentences, when judging the sentiment of a document.
	With this idea, we propose a cascading multiway attention (CMA) model, where 
	multiple ways of using user and product information are cascaded to influence
	the generation of attentions on the word and sentence layers. Then, sentences
	and documents are well modeled by multiple representation vectors, which
	provide rich information for sentiment classification. Experiments on IMDB and
	Yelp datasets demonstrate the effectiveness of our model.},
  url       = {http://www.aclweb.org/anthology/I17-1064}
}