Capturing User and Product Information for Document Level Sentiment Analysis with Deep Memory Network

Zi-Yi Dou


Abstract
Document-level sentiment classification is a fundamental problem which aims to predict a user’s overall sentiment about a product in a document. Several methods have been proposed to tackle the problem whereas most of them fail to consider the influence of users who express the sentiment and products which are evaluated. To address the issue, we propose a deep memory network for document-level sentiment classification which could capture the user and product information at the same time. To prove the effectiveness of our algorithm, we conduct experiments on IMDB and Yelp datasets and the results indicate that our model can achieve better performance than several existing methods.
Anthology ID:
D17-1054
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:
521–526
Language:
URL:
https://www.aclweb.org/anthology/D17-1054
DOI:
10.18653/v1/D17-1054
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PDF:
http://aclanthology.lst.uni-saarland.de/D17-1054.pdf