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 (translated)
在产品评论的情绪分析（SA）中，用户和产品信息都被证明是有用的。当前任务在统一模型中处理用户简档和产品信息，这可能无法有效地学习用户和产品的显着特征。在这项工作中，我们提出了一个双用户和产品内存网络（DUPMN）模型，以使用单独的内存网络来学习用户配置文件和产品评论。然后，两个表示联合用于情绪预测。使用单独的模型旨在更有效地捕获用户配置文件和产品信息。与最先进的统一预测模型相比，对三个基准数据集IMDB，Yelp13和Yelp14的评估表明，我们的双学习模型分别提供了0.6％，1.2％和0.9％的性能提升。通过p值测量的改进也被认为是非常显着的。
URL
https://arxiv.org/abs/1809.05807
