GTI at SemEval-2016 Task 5: SVM and CRF for Aspect Detection and Unsupervised Aspect-Based Sentiment Analysis

Proceedings of SemEval-2016, pages 306–311, San Diego, California, June 16-17, 2016. c⃝2016 Association for Computational Linguistics GTI at SemEval-2016 Task 5: SVM and CRF for Aspect Detection and Unsupervised Aspect-Based Sentiment Analysis Tamara ´Alvarez-L´opez, Jonathan Juncal-Mart´ınez, Milagros Fern´andez-Gavilanes Enrique Costa-Montenegro, Francisco Javier Gonz´alez-Casta˜no GTI Research Group AtlantTIC Centre, School of Telecommunication Engineering, University of Vigo 36310 Vigo, Spain {talvarez, jonijm, milagros.fernandez, kike}@gti.uvigo.es, javier@det.uvigo.es Abstract This paper describes in detail the approach carried out by the GTI research group for Se- mEval 2016 Task 5: Aspect-Based Sentiment Analysis, for the different subtasks proposed, as well as languages and dataset contexts. In particular, we developed a system for category detection based on SVM. Then for the opinion target detection task we developed a system based on CRFs. Both are built for restaurants domain in English and Spanish languages. Fi- nally for aspect-based sentiment analysis we carried out an unsupervised approach based on lexicons and syntactic dependencies, in En- glish language for laptops and restaurants do- mains. 1 Introduction In the last years, with the growth of Internet, peo- ple use it as a means of expressing their opinions and experiences about several subjects. That is the reason why there is a great amount of user generated information available online, through many different platforms, such as blogs, social networks, etc. This information became very valuable for companies, politicians, etc., who are interested in what users say about them or their products. Due to this, Sentiment Analysis (SA) techniques have attracted the interest of researches, trying to process all this amount of in- formation by means of usually supervised methods based on classifiers. Most of these researches focus on extracting the sentiment of a whole review or text (Liu, 2012). This is enough for many applications and purposes. However, sometimes there is a need for analysing the text in a deeper way, at entity or aspect level. For example, a review in the restaurants domain can include different opinions about different aspects, such as the service or the food quality, so it is inter- esting to distinguish the different opinions for each of these aspects. This is the reason why some studies emerged about the so-called aspect-based sentiment analysis (Marcheggiani et al., 2014; Lu et al., 2011). Hence this is the subject of the task 5 of the Se- mEval 2016 (Pontiki et al., 2016), divided into dif- ferent subtasks. Groups are asked to detect aspect categories in a review or sentence, which are prede- fined for each domain and formed by an entity and an attribute. Then, there is a subtask which consists of detecting the opinion target expression, which are related to the categories found. Finally, aspect-based sentiment analysis is required for one of the sub- tasks, associating a polarity, which can be positive, negative or neutral, to each of the categories found in the sentence or review. Datasets in different lan- guages and domains are available for proving the ap- proaches. The remainder of this paper is structured as fol- lows. …

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