Papers › Dual Memory Network Model for Biased Product Review Classification
Dual Memory Network Model for Biased Product Review Classification
Yunfei Long, Mingyu Ma, Qin Lu, Rong Xiang, Chu-Ren Huang
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.
Code
No code repository is listed for this paper in the archive or in Syntology's graph.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Sentiment Analysis | User and product information | DUPMN | IMDB (Acc) | 53.9 | #6 of 10 | Archive leaderboard | report |
| Sentiment Analysis | User and product information | DUPMN | Yelp 2013 (Acc) | 66.2 | #6 of 10 | Archive leaderboard | report |
| Sentiment Analysis | User and product information | DUPMN | Yelp 2014 (Acc) | 67.6 | #6 of 10 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections