SINAI: Syntactic Approach for Aspect-Based Sentiment Analysis

Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pages 730–735, Denver, Colorado, June 4-5, 2015. c⃝2015 Association for Computational Linguistics SINAI: Syntactic approach for Aspect Based Sentiment Analysis Salud M. Jim´enez-Zafra, Eugenio Mart´ınez-C´amara, M. Teresa Mart´ın-Valdivia, L. Alfonso Ure˜na-L´opez SINAI Research Group University of Ja´en E-23071, Ja´en (Spain) {sjzafra, emcamara, maite, laurena}@ujaen.es Abstract This paper describes the participation of the SINAI research group in the task Aspect Based Sentiment Analysis of SemEval Work- shop 2015 Edition. We propose a syntactic approach for identifying the words that mod- ify each aspect, with the aim of classifying the sentiment expressed towards each attribute of an entity. 1 Introduction Opinion Mining (OM), also known as Sentiment Analysis (SA), is the discipline that focuses on the computational treatment of opinion, sentiment and subjectivity in texts (Pang and Lee, 2008). Cur- rently, OM is a trendy task in the field of Natural Language Processing, due mainly to the fact of the proliferation of user-generated content and the in- terest in the knowledge of the opinion of people by consumers and businesses. Most of the systems developed up to now carry out opinion analysis at document level ((Pang et al., 2002), (Turney, 2002)) or at sentence level ((Wilson et al., 2005), (Yu and Hatzivassiloglou, 2003)), that is, they determine the overall sentiment expressed by the reviewer about the topic, product, person.. .of study. However, the fact that the overall sentiment of a product is positive does not mean that the author thinks that all the aspects of the product are posi- tives, or the fact that is negative does not involve that everything about the product is bad. For this reason, users and companies are not satisfied with knowing the overall sentiment of a product or service, they seek a more detailed knowledge. Consequently, to achieve a higher level of detail, part of the scientific community related to this area is working on SA at aspect level ((Quan and Ren, 2014), (Marcheggiani et al., 2014), (Lu et al., 2011), (Thet et al., 2010)) and even, there is a competition on this topic that began to conduct last year (Pontiki et al., 2014) in the International Workshop on Semantic Evaluation 2014 (SemEval 2014). This year, the 2015 edition of SemEval has also proposed a task for SA at aspect level. The SemEval-2015 Aspect Based Sentiment Analysis task is a continuation of SemEval-2014 Task 4 (Pon- tiki et al., 2014). The aim of this task is to identify the attributes of an entity that are being reviewed and the sentiment expressed for each one. It is di- vided into three slots. The first one is focused on the identification of every entity E and attribute A pair (E#A) towards which an opinion is expressed in the given text. Slot 2 proposes to determine the expression used in the text to refer to the reviewed entity, that is, the Opinion Target Expression (OTE). Finally, Slot 3 has as goal to classify the sentiment expressed over each …

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