# Dual Memory Network Model for Biased Product Review Classification

arXiv: https://arxiv.org/abs/1809.05807 · HTML: https://mlsanity.com/paper/1809.05807/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Sep 2018.

## Built on by (most cited first, 2 of 2)

- [Efficient Attribute Injection for Pretrained Language Models](https://mlsanity.com/paper/2109.07953/) (2021)
- [Exploiting Rich Textual User-Product Context for Improving Sentiment Analysis](https://mlsanity.com/paper/2212.08888/) (2022)

## Abstract (arXiv)

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.