Using Sentiment Analysis to Evaluate Qualitative Students' Responses

Autor/inn/en	Dake, Delali Kwasi; Gyimah, Esther
Titel	Using Sentiment Analysis to Evaluate Qualitative Students' Responses
Quelle	In: Education and Information Technologies, 28 (2023) 4, S.4629-4647 (19 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Dake, Delali Kwasi)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1360-2357
DOI	10.1007/s10639-022-11349-1
Schlagwörter	Feedback (Response); College Students; Psychological Patterns; Algorithms; Data Analysis; Artificial Intelligence; Foreign Countries; Course Evaluation; Ghana + Suchen Sie Ihr Suchwort? Collegestudent; Algorithm; Algorithmus; Auswertung; Künstliche Intelligenz; Ausland
Abstract	Text analytics in education has evolved to form a critical component of the future SMART campus architecture. Sentiment analysis and qualitative feedback from students is now a crucial application domain of text analytics relevant to institutions. The implementation of sentiment analysis helps understand learners' appreciation of lessons, which they prefer to express in long texts with little or no restriction. Such expressions depict the learner's emotions and mood during class engagements. This research deployed four classifiers, including Naïve Bayes (NB), Support Vector Machine (SVM), J48 Decision Tree (DT), and Random Forest (RF), on a qualitative feedback text after a semester-based course session at the University of Education, Winneba. After enough training and testing using the k-fold cross-validation technique, the SVM classification algorithm performed with a superior accuracy of 63.79%. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01