Sentiment analysis is a burgeoning field in natural language processing used to extract and categorize opinion in evaluative documents. We look at recommendation letters, which pose unique challenges to standard sentiment analysis systems. Our dataset is eighteen letters from applications to UMass Worcester Memorial Medical Center’s residency program in Obstetrics and Gynecology. Given a small dataset, we develop a method intended for use by domain experts to systematically explore their intuitions about the topical make-up of documents on which they make critical decisions. By leveraging WordNet and the WordNet Propagation algorithm, the method allows a user to develop topic seed sets from real data and propagate them into robust lexicons for use on new data. We show how one pass through the method yields useful feedback to our beliefs about the make-up of recommendation letters. At the end, future directions are outlined which assume a fuller dataset.