@article {Faizi202252,
	title = {A Sentiment Analysis Based Approach for Exploring Student Feedback},
	journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {13449 LNCS},
	year = {2022},
	note = {cited By 0},
	pages = {52-59},
	abstract = {Student feedback is commonly used as a reliable source of information to evaluate learning outcomes and teaching quality. This feedback has proven to provide faculty not only with valuable insights into how students are learning, but also with an ideal opportunity to reflect on teaching resources and instructional strategies. However, given the increasing massive amounts of feedback that is available online, collecting and analyzing this data manually is not usually an easy task. The aim of this work is, therefore, to put forward a sentiment analysis classifier that is capable of categorizing student feedback as being either positive or negative. To this end, students{\textquoteright} reviews posted about online courses were automatically extracted, preprocessed and then fed into various machine learning algorithms. The findings of this analysis revealed that the Support Vector Machines (SVM) algorithm achieves the highest accuracy score (93.35\%) and, thus, outperforms other implemented models. {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.},
	keywords = {Analysis-based approaches, Education computing, Learning algorithms, Learning outcome, Machine-learning, Outcome quality, Quality control, Sentiment analysis, Sources of informations, Student feedback, Students, Support vector machines, Support vectors machine, Teaching quality, Teaching resources},
	doi = {10.1007/978-3-031-15273-3_6},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137997385\&doi=10.1007\%2f978-3-031-15273-3_6\&partnerID=40\&md5=feac3688f39fab9ad1f85464953459ec},
	author = {Faizi, R. and El Fkihi, S.}
}
