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ERIC Number: EJ1109317
Record Type: Journal
Publication Date: 2015-Dec
Pages: 14
Abstractor: As Provided
ISSN: EISSN-2330-8516
Automated Analysis of Text in Graduate School Recommendations. Research Report. ETS RR-15-23
Heilman, Michael; Breyer, F. Jay; Williams, Frank; Klieger, David; Flor, Michael
ETS Research Report Series, Dec 2015
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.
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Publication Type: Journal Articles; Reports - Research
Education Level: Higher Education; Postsecondary Education
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Grant or Contract Numbers: N/A