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.

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Every day more companies are interested in users' opinions about their products or services. Also, every day there are more users that search for reviews on the web before purchasing a product. These users and companies are not satisfied with knowing the overall sentiment of a product, they want a finer knowledge of users' opinions. Owing to this fact, more and more researchers are working on sentiment analysis at aspect-level. This paper describes an unsupervised approach for aspect-based sentiment analysis, which aims to identify the aspects of given target entities and the sentiment expressed for each aspect. We have evaluated several tasks, although perhaps the major novelty is in the classification of the aspects. We employ a lexicon-based method combining different linguistic resources and we conclude that the combination of several classifiers improves the classification significantly. In addition, a comparison with a supervised system is performed in order to determine the strengths and weakness of each of them.

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