Capturing User and Product Information for Document Level Sentiment Analysis with Deep Memory Network

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 521–526 Copenhagen, Denmark, September 7–11, 2017. c⃝2017 Association for Computational Linguistics Capturing User and Product Information for Document Level Sentiment Analysis with Deep Memory Network Zi-Yi Dou National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, 210023, China 141242042@smail.nju.edu.cn Abstract Document-level sentiment classification is a fundamental problem which aims to pre- dict a user’s overall sentiment about a product in a document. Several methods have been proposed to tackle the problem whereas most of them fail to consider the influence of users who express the senti- ment and products which are evaluated. To address the issue, we propose a deep mem- ory network for document-level sentiment classification which could capture the user and product information at the same time. To prove the effectiveness of our algo- rithm, we conduct experiments on IMDB and Yelp datasets and the results indicate that our model can achieve better perfor- mance than several existing methods. 1 Introduction Sentiment analysis, sometimes known as opinion mining, is the field of study that analyzes peo- ple’s opinions, sentiments, evaluations, attitudes and emotions from written language. It is one of the most active and critical research areas in natu- ral language processing (Liu, 2012). On the one hand, from the industry point of view, knowing the feelings among consumers based on their com- ments is beneficial and may support strategic mar- ket decisions. On the other hand, potential cus- tomers are often interested in other people’s opin- ion in order to find out the choices that best fits their preferences (Moraes et al., 2013). Previous studies tackled the sentiment analysis problem at various levels of granularity, from doc- ument level to sentence level due to different ob- jectives of applications (Zhang et al., 2009). In this work, we mainly focus on document-level senti- ment classification Basically, the task is to predict user’s overall sentiment or polarity in a document about a product (Pang and Lee, 2008). Most existing methods mainly utilize local text information whereas ignoring the influences of users and products (Tang et al., 2015). As is often the case, there are certain consistencies for both users and products. To illustrate, lenient users may always give higher ratings than fastidious ones even if they post the same review. Also, it is not surprising that some products may always receive low ratings because of their poor quality and vice versa. Therefore, it is necessary to leverage indi- vidual preferences of users and overall qualities of products in order to achieve better performance. Tang et al. (2015) proposed a novel method dubbed User Product Neural Network (UPNN) which capture user- and product-level information for sentiment classification. Their approach has shown great promise but one major drawback of their work is that for users and products with lim- ited information, it is hard to train the representa- tion vector and matrix for them. Inspired by the recent success of computa- tional models with attention mechanism and …

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