What is it about?
The model was tested at the Technical University of Manabi (Ecuador) using the machine learning method with a data set in Spanish. The best-performing classification algorithm was support vector machine with 0.870 in F-Measure for its training features on short texts. The early results show that the model classifies feedback as positive or negative.
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Why is it important?
Useful tool to encourage reflection and criticality skills in the teaching-learning process
Perspectives
The purpose of this research is to offer higher education institutions, professors, and researchers, a peer feedback sentiment analysis model as an alternative to analyze subjectivity and improve the opportunity for formative and summative evaluation.
Maricela Pinargote Ortega
Universidad Tecnica de Manabi
Read the Original
This page is a summary of: Sentiment analysis of peer feedback in higher education, January 2024, American Institute of Physics,
DOI: 10.1063/5.0187956.
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