Off-campus UMass Amherst users: To download campus access theses, please use the following link to log into our proxy server with your UMass Amherst user name and password.

Non-UMass Amherst users: Please talk to your librarian about requesting this thesis through interlibrary loan.

Theses that have an embargo placed on them will not be available to anyone until the embargo expires.

Document Type

Open Access

Degree Program

Linguistics

Degree Type

Master of Arts (M.A.)

Year Degree Awarded

2012

Month Degree Awarded

May

Keywords

Sentiment analysis, recommendation letters

Abstract

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.

First Advisor

Brian Dillon

Share

COinS