Aspect based sentiment analysis (ABSA) is an opinion mining process where texts are analyzed to extract the sentiments that their authors express towards certain features and characteristics of particular entities, such as products or persons. Key role in the effectiveness of this process plays the accurate and complete identification of the entities’ discussed aspects within the text, as well as of the evaluation expressions that accompany these aspects. Nevertheless, what entities may be considered as aspects and what evaluation expressions may characterize them, depends largely on the domain at hand. With that in mind, in this paper we propose an approach for representing and populating semantic lexicons that contain domain-specific aspect-evaluation-polarity relations and, as such, can be (re-)used towards more effective ABSA in concrete domains and scenarios.