@InProceedings{long-EtAl:2017:EMNLP20171,
  author    = {Long, Yunfei  and  Qin, Lu  and  Xiang, Rong  and  Li, Minglei  and  Huang, Chu-Ren},
  title     = {A Cognition Based Attention Model for 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     = {462--471},
  abstract  = {Attention models are proposed in sentiment analysis because some words are more
	important than others. However,most existing methods either use local context
	based text information or user preference information. In this work, we propose
	a novel attention model trained by cognition grounded eye-tracking data. A
	reading prediction model is first built using eye-tracking data as dependent
	data and other features in the context as independent data. The predicted
	reading time is then used to build a cognition based attention (CBA) layer for
	neural sentiment analysis. As a comprehensive model, We can capture attentions
	of words in sentences as well as sentences in documents. Different attention
	mechanisms can also be incorporated to capture other aspects of attentions.
	Evaluations show the CBA based method outperforms the state-of-the-art local
	context based attention methods significantly. This brings insight to how
	cognition grounded data can be brought into NLP tasks.
	Author{4}{Affiliation}},
  url       = {https://www.aclweb.org/anthology/D17-1048}
}