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Title: A cognition based attention model for sentiment analysis
Authors: Long, Y 
Lu, Q 
Xiang, R
Li, M 
Huang, CR 
Issue Date: 2017
Source: In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, EMNLP 2017, Copenhagen, Denmark, 9-11 September 2017, p. 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.
Publisher: Association for Computational Linguistics (ACL)
ISBN: 9.78E+12
DOI: 10.18653/v1/d17-1048
Rights: © 2017 Association for Computational Linguistics
ACL materials are Copyright © 1963–2021 ACL; other materials are copyrighted by their respective copyright holders. Materials prior to 2016 here are licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 International License. Permission is granted to make copies for the purposes of teaching and research. Materials published in or after 2016 are licensed on a Creative Commons Attribution 4.0 International License (
The following publication Long, Y., Lu, Q., Xiang, R., Li, M., & Huang, C. -. (2017). A cognition based attention model for sentiment analysis. Paper presented at the EMNLP 2017 - Conference on Empirical Methods in Natural Language Processing, Proceedings, 462-471 is available at
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