Recursive Neural Conditional Random Fields for Aspect-based Sentiment Analysis

Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 616–626, Austin, Texas, November 1-5, 2016. c⃝2016 Association for Computational Linguistics Recursive Neural Conditional Random Fields for Aspect-based Sentiment Analysis Wenya Wang†‡ Sinno Jialin Pan† Daniel Dahlmeier‡ Xiaokui Xiao† †Nanyang Technological University, Singapore ‡SAP Innovation Center Singapore †{wa0001ya, sinnopan, xkxiao}@ntu.edu.sg, ‡{d.dahlmeier}@sap.com Abstract In aspect-based sentiment analysis, extract- ing aspect terms along with the opinions be- ing expressed from user-generated content is one of the most important subtasks. Previ- ous studies have shown that exploiting con- nections between aspect and opinion terms is promising for this task. In this paper, we pro- pose a novel joint model that integrates recur- sive neural networks and conditional random fields into a unified framework for explicit as- pect and opinion terms co-extraction. The proposed model learns high-level discrimina- tive features and double propagates informa- tion between aspect and opinion terms, simul- taneously. Moreover, it is flexible to incor- porate hand-crafted features into the proposed model to further boost its information extrac- tion performance. Experimental results on the dataset from SemEval Challenge 2014 task 4 show the superiority of our proposed model over several baseline methods as well as the winning systems of the challenge. 1 Introduction Aspect-based sentiment analysis (Pang and Lee, 2008; Liu, 2011) aims to extract important infor- mation, e.g., opinion targets, opinion expressions, target categories, and opinion polarities, from user- generated content, such as microblogs, reviews, etc. This task was first studied by Hu and Liu (2004a; 2004b), followed by Popescu and Etzioni (2005), Zhuang et al. (2006), Zhang et al. (2010), Qiu et al. (2011), Li et al. (2010). In aspect-based senti- ment analysis, one of the goals is to extract explicit aspects of an entity from text, along with the opin- ions being expressed. For example, in a restaurant review “I have to say they have one of the fastest de- livery times in the city.”, the aspect term is delivery times, and the opinion term is fastest. Among previous work, one of the approaches is to accumulate aspect and opinion terms from a seed collection without label information, by utiliz- ing syntactic rules or modification relations between them (Qiu et al., 2011; Liu et al., 2013b). In the above example, if we know fastest is an opinion word, then delivery times is probably inferred to be an aspect because fastest is its modifier. However, this approach largely relies on hand-coded rules and is restricted to certain Part-of-Speech (POS) tags, e.g., opinion words are restricted to be adjectives. Another approach focuses on feature engineering based on predefined lexicons, syntactic analysis, etc. (Jin and Ho, 2009; Li et al., 2010). A sequence labeling classifier is then built to extract aspect and opinion terms. This approach requires extensive ef- forts for designing hand-crafted features and only combines features linearly for classification which ignores higher order interactions. To overcome the limitations of existing methods, we propose a novel model, named Recursive Neu- ral Conditional Random Fields (RNCRF). Specif- ically, …

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