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A Sentiment Analysis Based Approach for Exploring Student Feedback

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Innovative Technologies and Learning (ICITL 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13449))

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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’ 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.

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Authors and Affiliations

  1. ENSIAS, Mohammed V University in Rabat, Rabat, Morocco

    Rdouan Faizi & Sanaa El Fkihi

Authors
  1. Rdouan Faizi
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  2. Sanaa El Fkihi
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Corresponding author

Correspondence to Rdouan Faizi .

Editor information

Editors and Affiliations

  1. National Cheng Kung University, Tainan City, Taiwan

    Yueh-Min Huang

  2. Southern Taiwan University of Science and Technology, Tainan City, Taiwan

    Shu-Chen Cheng

  3. University of Trás-os-Montes and Alto Douro, Vila Real, Portugal

    JoĂŁo Barroso

  4. OsloMet – Oslo Metropolitan University, Oslo, Norway

    Frode Eika Sandnes

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Faizi, R., El Fkihi, S. (2022). A Sentiment Analysis Based Approach for Exploring Student Feedback. In: Huang, YM., Cheng, SC., Barroso, J., Sandnes, F.E. (eds) Innovative Technologies and Learning. ICITL 2022. Lecture Notes in Computer Science, vol 13449. Springer, Cham. https://doi.org/10.1007/978-3-031-15273-3_6

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  • DOI: https://doi.org/10.1007/978-3-031-15273-3_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-15272-6

  • Online ISBN: 978-3-031-15273-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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