Learning user and product distributed representations using a sequence model for sentiment analysis


In product reviews, it is observed that the distribution of polarity ratings over reviews written by different users or evaluated based on different products are often skewed in the real world. As such, incorporating user and product information would be helpful for the task of sentiment classification of reviews. However, existing approaches ignored the temporal nature of reviews posted by the same user or evaluated on the same product. We argue that the temporal relations of reviews might be potentially useful for learning user and product embedding and thus propose employing a sequence model to embed these temporal relations into user and product representations so as to improve the performance of document-level sentiment analysis. Specifically, we first learn a distributed representation of each review by a one-dimensional convolutional neural network. Then, taking these representations as pretrained vectors, we use a recurrent neural network with gated recurrent units to learn distributed representations of users and products. Finally, we feed the user, product and review representations into a machine learning classifier for sentiment classification. Our approach has been evaluated on three large-scale review datasets from the IMDB and Yelp. Experimental results show that: (1) sequence modeling for the purposes of distributed user and product representation learning can improve the performance of document-level sentiment classification; (2) the proposed approach achieves state-of-The-Art results on these benchmark datasets.

Publication DOI: https://doi.org/10.1109/MCI.2016.2572539
Divisions: College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
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Uncontrolled Keywords: Artificial Intelligence,Theoretical Computer Science
Publication ISSN: 1556-603X
Last Modified: 10 Jun 2024 07:17
Date Deposited: 18 Aug 2016 09:05
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Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
http://ieeexplo ... rnumber=7515294 (Publisher URL)
PURE Output Type: Article
Published Date: 2016-08
Published Online Date: 2016-07-18
Accepted Date: 2016-05-19
Submitted Date: 2016-05-18
Authors: Chen, Tao
Xu, Ruifeng
He, Yulan (ORCID Profile 0000-0003-3948-5845)
Xia, Yunqing
Wang, Xuan



Version: Accepted Version

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