We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Automated Analysis of Text in Graduate School Recommendations.
- Authors
Heilman, Michael; Breyer, F. Jay; Williams, Frank; Klieger, David; Flor, Michael
- Abstract
Graduate school recommendations are an important part of admissions in higher education, and natural language processing may be able to provide objective and consistent analyses of recommendation texts to complement readings by faculty and admissions staff. However, these sorts of high-stakes, personal recommendations are different from the product and service reviews studied in much of the research on sentiment analysis. In this report, we develop an approach for analyzing recommendations and evaluate the approach on four tasks: (a) identifying which sentences are actually about the student, (b) measuring specificity, (c) measuring sentiment, and (d) predicting recommender ratings. We find substantial agreement with human annotations and analyze the effects of different types of features.
- Subjects
GRADUATE education; GRADUATE students; SENTIMENT analysis; UNIVERSITY &; college admission
- Publication
ETS Research Reports Series, 2015, Vol 2015, Issue 2, p1
- ISSN
2330-8516
- Publication type
Article
- DOI
10.1002/ets2.12070