bunji at SemEval-2016 Task 5: Neural and Syntactic Models of Entity-Attribute Relationship for Aspect-based Sentiment Analysis

Proceedings of SemEval-2016, pages 289–295, San Diego, California, June 16-17, 2016. c⃝2016 Association for Computational Linguistics bunji at SemEval-2016 Task 5: Neural and Syntactic Models of Entity-Attribute Relationship for Aspect-based Sentiment Analysis Toshihiko Yanase, Kohsuke Yanai, Misa Sato, Toshinori Miyoshi, Yoshiki Niwa Research & Development Group, Hitachi, Ltd. {toshihiko.yanase.gm, kohsuke.yanai.cs, misa.sato.mw, toshinori.miyoshi.pd, yoshiki.niwa.tx}@hitachi.com Abstract This paper describes a sentiment analysis sys- tem developed by the bunji team in SemEval- 2016 Task 5. In this task, we estimate the sentimental polarity of a given entity-attribute (E#A) pair in a sentence. Our approach is to estimate the relationship between target enti- ties and sentimental expressions. We use two different methods to estimate the relationship. The first one is based on a neural attention model that learns relations between tokens and E#A pairs through backpropagation. The sec- ond one is based on a rule-based system that examines several verb-centric relations related to E#A pairs. We confirmed the effective- ness of the proposed methods in a target es- timation task and a polarity estimation task in the restaurant domain, while our overall ranks were modest. 1 Introduction Sentiment analysis is an important technology for understanding users’ intentions from review texts. Such technologies are also useful for argumentation mining because it is necessary for readers to cap- ture targets of interest and their polarities (Sato et al., 2015). Shared tasks of aspect-based sentiment analysis (ABSA) in SemEval provide a test bed for fine-grained analysis of sentiment polarities (Pontiki et al., 2014; Pontiki et al., 2015; Pontiki et al., 2016). We participate in all four slots (Slot 1, 2, 1 & 2, and 3) of the restaurant domain and laptop domain in English. We focus on two types of models to capture the entity-attribute relationship, especially in Slot 2 and Slot 3. The first one is a neural network based model. The second one is a rule-based approach. Now, we explain the problem settings of the slots and our approaches. The following is an example of sentences that provide positive opinions to the FOOD#QUALITY aspect: Pizza here is good. Slot 1 is an extraction of all aspects mentioned in a sentence. In this example, the goal is to choose FOOD#QUALITY among many other aspects. We formulate the problem as a multi-label classification problem and use a neural network-based model. Slot 2 is an extraction of opinion target expressions. The expected output is “Pizza” in the above example. We use a pattern matching based approach and fo- cus on gathering resources such as dictionaries. For Slot 1 & 2, we simply combine the prediction re- sults of Slot 1 and Slot 2. Slot 3 is an estimation of sentiment polarities. In this example, we esti- mate the polarity of this sentence from the aspect of FOOD#QUALITY. For Slot 3, we take two ap- proaches. The first approach is a neural attention model (Luong et al., 2015) that considers the entity attention (FOOD) and attribute attention (QUAL- ITY) of each token. The second approach is a pat- tern …

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