Incorporating student opinion into opinion mining: A student-sourced sentiment analysis classifier

Hillaire, Garron; Rienties, Bart; Fenton-O’Creevy, Mark; Zdrahal, Zdenek and Tempelaar, Dirk (2022). Incorporating student opinion into opinion mining: A student-sourced sentiment analysis classifier. In: Rienties, Bart; Hampel, Regine; Scanlon, Eileen and Whitelock, Denise eds. Open World Learning: Research, Innovation and the Challenges of High-Quality Education. Routledge Research in Digital Education and Educational Technology. New York, USA: Routledge, pp. 171–185.

DOI: https://doi.org/10.4324/9781003177098-15

Abstract

To build a better understanding of the online student experience, there is promise in exploring emotional measures. The first step in gaining clarity on the role of emotions in online learning is understanding the accuracy of emotional measures. As text is ubiquitous in online learning, in Chapter 13 we reviewed an emotional measure of sentiment analysis which can interpret text for emotional expression. In order to better understand the experience of students through text we take a clear theoretical stance that emotion is socially constructed and that text can be categorized as positive, negative, neutral, or mixed. We trained a student-sourced sentiment analysis classifier by using crowd-sourcing methods with students, and benchmarked it with traditional crowd-sourcing techniques. Our results show that our student-sourced classifier did a better job of predicting future student labels as compared to our benchmarks. In addition, interviews with students demonstrated that five out of six students found our student-sourced classifier useful. The results of this study raised questions about how ground truth is established for sentiment analysis and advocates the utility of anchoring ground truth to the student experience.

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