@InProceedings{chen-EtAl:2017:EMNLP20171,
  author    = {Chen, Peng  and  Sun, Zhongqian  and  Bing, Lidong  and  Yang, Wei},
  title     = {Recurrent Attention Network on Memory for Aspect Sentiment Analysis},
  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     = {452--461},
  abstract  = {We propose a novel framework based on neural networks to identify the sentiment
	of opinion targets in a comment/review. Our framework adopts multiple-attention
	mechanism to capture sentiment features separated by a long distance, so that
	it is more robust against irrelevant information. The results of multiple
	attentions are non-linearly combined with a recurrent neural network, which
	strengthens the expressive power of our model for handling more complications.
	The weighted-memory mechanism not only helps us avoid the labor-intensive
	feature engineering work, but also provides a tailor-made memory for different
	opinion targets of a sentence. We examine the merit of our model on four
	datasets: two are from SemEval2014, i.e. reviews of restaurants and laptops; a
	twitter dataset, for testing its performance on social media data; and a
	Chinese news comment dataset, for testing its language sensitivity. The
	experimental results show that our model consistently outperforms the
	state-of-the-art methods on different types of data.},
  url       = {https://www.aclweb.org/anthology/D17-1047}
}