There’s So Much to Do and Not Enough Time to Do It! A Case for Sentiment Analysis to Derive Meaning From Open Text Using Student Reflections of Engineering Activities

Bibliographic Data

ID12470932
AuthorsAbhik Roy (0000-0001-5074-409X, West Virginia University, corresponding author), Karen E Rambo‐hernandez (0000-0001-8107-2898, Texas A&M University)
Year2021
Volume42
Issue4
Pages559-576
Publication date2021-10-19
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueAmerican Journal of Evaluation (JOURNAL)
Journal identifiersISSN: 1098-2140 • E-ISSN: 1557-0878
PublisherSAGE Publishing (PUBLISHER • US)
DOI10.1177/1098214020962576
OpenAlexW3210433348
LanguageEN
Citations received2
References cited25

Evaluators often find themselves in situations where resources to conduct thorough evaluations are limited. In this paper, we present a familiar instance where there is an overwhelming amount of open text to be analyzed under the constraints of time and personnel. In instances when timely feedback is important, the data are plentiful, and answers to the study questions carry lower consequences, we build a case for using a machine learning, in particular a sentiment analysis. We begin by explaining the rationale for the use of sentiment analysis and provide an introduction to this method. Next, we provide an example of a sentiment analysis leveraging data collected from a program evaluation of an engineering education intervention, specifically to text extracted from student reflections of course activities. Finally, limitations of sentiment analysis and related techniques are discussed as well as areas for future research

Content analysis · Data science · Meaning (existential · Sentiment analysis · Sociology · Computer Science · Psychology · Sentiment Analysis and Opinion Mining · Software Engineering Research · Topic Modeling · Artificial Intelligence

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Open DOI