TY - GEN
T1 - Dual Memory Network Model for Biased Product Review Classification
AU - Long, Yunfei
AU - Ma, Mingyu
AU - Lu, Qin
AU - Xiang, Rong
AU - Huang, Chu Ren
N1 - Publisher Copyright:
© 2018 Association for Computational Linguistics
PY - 2018/10
Y1 - 2018/10
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85122293093&partnerID=8YFLogxK
U2 - 10.18653/v1/W18-6220
DO - 10.18653/v1/W18-6220
M3 - Conference article published in proceeding or book
AN - SCOPUS:85122293093
T3 - WASSA 2018 - 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, Proceedings of the Workshop
SP - 140
EP - 148
BT - WASSA 2018 - 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, Proceedings of the Workshop
A2 - Balahur, Alexandra
A2 - Mohammad, Saif M.
A2 - Hoste, Veronique
A2 - Klinger, Roman
PB - Association for Computational Linguistics (ACL)
T2 - 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, WASSA 2018
Y2 - 31 October 2018
ER -