@inproceedings{long-etal-2018-dual,
title = "Dual Memory Network Model for Biased Product Review Classification",
author = "Long, Yunfei and
Ma, Mingyu and
Lu, Qin and
Xiang, Rong and
Huang, Chu-Ren",
editor = "Balahur, Alexandra and
Mohammad, Saif M. and
Hoste, Veronique and
Klinger, Roman",
booktitle = "Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
month = oct,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-6220",
doi = "10.18653/v1/W18-6220",
pages = "140--148",
abstract = "In sentiment analysis (SA) of product reviews, both user and product information are proven to be useful. Current tasks handle user profile and product information in a unified model which may not be able to learn salient features of users and products effectively. In this work, we propose a dual user and product memory network (DUPMN) model to learn user profiles and product reviews using separate memory networks. Then, the two representations are used jointly for sentiment prediction. The use of separate models aims to capture user profiles and product information more effectively. Compared to state-of-the-art unified prediction models, the evaluations on three benchmark datasets, IMDB, Yelp13, and Yelp14, show that our dual learning model gives performance gain of 0.6{\%}, 1.2{\%}, and 0.9{\%}, respectively. The improvements are also deemed very significant measured by \textit{p-values}.",
}
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<abstract>In sentiment analysis (SA) of product reviews, both user and product information are proven to be useful. Current tasks handle user profile and product information in a unified model which may not be able to learn salient features of users and products effectively. In this work, we propose a dual user and product memory network (DUPMN) model to learn user profiles and product reviews using separate memory networks. Then, the two representations are used jointly for sentiment prediction. The use of separate models aims to capture user profiles and product information more effectively. Compared to state-of-the-art unified prediction models, the evaluations on three benchmark datasets, IMDB, Yelp13, and Yelp14, show that our dual learning model gives performance gain of 0.6%, 1.2%, and 0.9%, respectively. The improvements are also deemed very significant measured by p-values.</abstract>
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%0 Conference Proceedings
%T Dual Memory Network Model for Biased Product Review Classification
%A Long, Yunfei
%A Ma, Mingyu
%A Lu, Qin
%A Xiang, Rong
%A Huang, Chu-Ren
%Y Balahur, Alexandra
%Y Mohammad, Saif M.
%Y Hoste, Veronique
%Y Klinger, Roman
%S Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
%D 2018
%8 October
%I Association for Computational Linguistics
%C Brussels, Belgium
%F long-etal-2018-dual
%X In sentiment analysis (SA) of product reviews, both user and product information are proven to be useful. Current tasks handle user profile and product information in a unified model which may not be able to learn salient features of users and products effectively. In this work, we propose a dual user and product memory network (DUPMN) model to learn user profiles and product reviews using separate memory networks. Then, the two representations are used jointly for sentiment prediction. The use of separate models aims to capture user profiles and product information more effectively. Compared to state-of-the-art unified prediction models, the evaluations on three benchmark datasets, IMDB, Yelp13, and Yelp14, show that our dual learning model gives performance gain of 0.6%, 1.2%, and 0.9%, respectively. The improvements are also deemed very significant measured by p-values.
%R 10.18653/v1/W18-6220
%U https://aclanthology.org/W18-6220
%U https://doi.org/10.18653/v1/W18-6220
%P 140-148
Markdown (Informal)
[Dual Memory Network Model for Biased Product Review Classification](https://aclanthology.org/W18-6220) (Long et al., WASSA 2018)
ACL
- Yunfei Long, Mingyu Ma, Qin Lu, Rong Xiang, and Chu-Ren Huang. 2018. Dual Memory Network Model for Biased Product Review Classification. In Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pages 140–148, Brussels, Belgium. Association for Computational Linguistics.