SemEval-2016 Task 5: Aspect Based Sentiment Analysis

Proceedings of SemEval-2016, pages 19–30, San Diego, California, June 16-17, 2016. c⃝2016 Association for Computational Linguistics SemEval-2016 Task 5: Aspect Based Sentiment Analysis Maria Pontiki∗1, Dimitrios Galanis1, Haris Papageorgiou1, Ion Androutsopoulos1,2, Suresh Manandhar3, Mohammad AL-Smadi4, Mahmoud Al-Ayyoub4, Yanyan Zhao5, Bing Qin5, Orphée De Clercq6, Véronique Hoste6, Marianna Apidianaki7, Xavier Tannier7, Natalia Loukachevitch8, Evgeny Kotelnikov9, Nuria Bel10, Salud María Jiménez-Zafra11, Gülşen Eryiğit12 1Institute for Language and Speech Processing, Athena R.C., Athens, Greece, 2Dept. of Informatics, Athens University of Economics and Business, Greece, 3Dept. of Computer Science, University of York, UK, 4Computer Science Dept., Jordan University of Science and Technology Irbid, Jordan, 5Harbin Institute of Technology, Harbin, Heilongjiang, P.R. China, 6LT3, Ghent University, Ghent, Belgium, 7LIMSI, CNRS, Univ. Paris-Sud, Université Paris-Saclay, Orsay, France, 8Lomonosov Moscow State University, Moscow, Russian Federation, 9Vyatka State University, Kirov, Russian Federation, 10Universitat Pompeu Fabra, Barcelona, Spain, 11Dept. of Computer Science, Universidad de Jaén, Spain, 12Dept. of Computer Engineering, Istanbul Technical University, Turkey Abstract This paper describes the SemEval 2016 shared task on Aspect Based Sentiment Analysis (ABSA), a continuation of the respective tasks of 2014 and 2015. In its third year, the task provided 19 training and 20 testing datasets for 8 languages and 7 domains, as well as a common evaluation procedure. From these datasets, 25 were for sentence-level and 14 for text-level ABSA; the latter was introduced for the first time as a subtask in SemEval. The task attracted 245 submissions from 29 teams. 1 Introduction Many consumers use the Web to share their experi- ences about products, services or travel destinations (Yoo and Gretzel, 2008). Online opinionated texts (e.g., reviews, tweets) are important for consumer decision making (Chevalier and Mayzlin, 2006) and constitute a source of valuable customer feedback that can help companies to measure satisfaction and improve their products or services. In this setting, Aspect Based Sentiment Analysis (ABSA) - i.e., mining opinions from text about specific entities and their aspects (Liu, 2012) - can provide valuable in- sights to both consumers and businesses. An ABSA ∗*Corresponding author: mpontiki@ilsp.gr. Figure 1: Table summarizing the average sentiment for each aspect of an entity. method can analyze large amounts of unstructured texts and extract (coarse- or fine-grained) informa- tion not included in the user ratings that are available in some review sites (e.g., Fig. 1). Sentiment Analysis (SA) touches every aspect (e.g., entity recognition, coreference resolution, negation handling) of Natural Language Processing (Liu, 2012) and as Cambria et al. (2013) mention “it requires a deep understanding of the explicit and im- plicit, regular and irregular, and syntactic and se- mantic language rules”. Within the last few years several SA-related shared tasks have been organized in the context of workshops and conferences focus- 19 ing on somewhat different research problems (Seki et al., 2007; Seki et al., 2008; Seki et al., 2010; Mitchell, 2013; Nakov et al., 2013; Rosenthal et al., 2014; Pontiki et al., 2014; Rosenthal et al., 2015; Ghosh et al., 2015; Pontiki et al., 2015; Moham- mad et al., 2016; Recupero and Cambria, …

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