A Cognition Based Attention Model for Sentiment Analysis

Yunfei Long, Qin Lu, Rong Xiang, Minglei Li, Chu-Ren Huang


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.
Anthology ID:
D17-1048
Volume:
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
462–471
Language:
URL:
https://www.aclweb.org/anthology/D17-1048
DOI:
10.18653/v1/D17-1048
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PDF:
http://aclanthology.lst.uni-saarland.de/D17-1048.pdf