Sentiment-Aspect Extraction based on Restricted Boltzmann Machines

Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, pages 616–625, Beijing, China, July 26-31, 2015. c⃝2015 Association for Computational Linguistics Sentiment-Aspect Extraction based on Restricted Boltzmann Machines Linlin Wang1, Kang Liu2∗, Zhu Cao1, Jun Zhao2 and Gerard de Melo1 1Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China 2National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China {ll-wang13, cao-z13}@mails.tsinghua.edu.cn, {kliu, jzhao}@nlpr.ia.ac.cn, gdm@demelo.org Abstract Aspect extraction and sentiment analysis of reviews are both important tasks in opinion mining. We propose a novel senti- ment and aspect extraction model based on Restricted Boltzmann Machines to jointly address these two tasks in an unsupervised setting. This model reflects the gener- ation process of reviews by introducing a heterogeneous structure into the hidden layer and incorporating informative priors. Experiments show that our model outper- forms previous state-of-the-art methods. 1 Introduction Nowadays, it is commonplace for people to ex- press their opinion about various sorts of entities, e.g., products or services, on the Internet, espe- cially in the course of e-commerce activities. Ana- lyzing online reviews not only helps customers ob- tain useful product information, but also provide companies with feedback to enhance their prod- ucts or service quality. Aspect-based opinion min- ing enables people to consider much more fine- grained analyses of vast quantities of online re- views, perhaps from numerous different merchant sites. Thus, automatic identification of aspects of entities and relevant sentiment polarities in Big Data is a significant and urgent task (Liu, 2012; Pang and Lee, 2008; Popescu and Etzioni, 2005). Identifying aspect and analyzing sentiment words from reviews has the ultimate goal of dis- cerning people’s opinions, attitudes, emotions, etc. towards entities such as products, services, orga- nizations, individuals, events, etc. In this con- text, aspect-based opinion mining, also known as feature-based opinion mining, aims at extracting and summarizing particular salient aspects of enti- ties and determining relevant sentiment polarities ∗Corresponding Author: Kang Liu (kliu@nlpr.ia.ac.cn) from reviews (Hu and Liu, 2004). Consider re- views of computers, for example. A given com- puter’s components (e.g., hard disk, screen) and attributes (e.g., volume, size) are viewed as aspects to be extracted from the reviews, while sentiment polarity classification consists in judging whether an opinionated review expresses an overall posi- tive or negative opinion. Regarding aspect identification, previous meth- ods can be divided into three main categories: rule-based, supervised, and topic model-based methods. For instance, association rule-based methods (Hu and Liu, 2004; Liu et al., 1998) tend to focus on extracting product feature words and opinion words but neglect connecting product features at the aspect level. Existing rule-based methods typically are not able to group the ex- tracted aspect terms into categories. Supervised (Jin et al., 2009; Choi and Cardie, 2010) and semi- supervised learning methods (Zagibalov and Car- roll, 2008; Mukherjee and Liu, 2012) were intro- duced to resolve certain aspect identification prob- lems. However, supervised training requires hand- labeled training data and …

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