A key question in sentiment analysis is whether sentiment ex-pressions, in a given text, are related to particular entities. This is an imperative question, since people are typically interested in sentiments on specific entities and not in the overall sentiment articulated in an article or a document. Sentiment relevance is aimed at addressing this precise problem. In this paper, we argue that exploiting information about the focus of the document on the entity of interest can significantly improve the task of detecting sentiment relevance and, hence, the final sentiment scores assigned for the entities. In order to assess the value of such information, we look at various methods for detecting sentiment relevance for entities. We consider both rule-based algorithms that rely on the entity's physical or syntactic proximity to the sentiment expressions as well as more sophisticated machine learning classification algorithms. We demonstrate that the focus of the document on the entities within it is, indeed, an important piece of information, which can be accurately learned with super-vised classification means. We, further, found that overall classification-based algorithms perform better than the deterministic ones in identifying sentiment relevance, with sequence-classification performing significantly better than direct classification.