Entity extraction and sentiment classification are among the most common types of information derived from documents, but the problem of directly associating entities and sentiment has received less attention. We use TextRank on a graph linking entities and sentiment-laden words and phrases. We extract from the resulting eigenvector the final sentiment weights of the entities. We then explore the algorithm's performance and accuracy, compared to a baseline.