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Discovering Sentiments and Latent Themes in the Views of Faculty Members towards the Shift from Conventional to Online Teaching Using VADER and Latent Dirichlet Allocation

Niel Francis B. Casillano

Abstract—This research primarily aimed at determining the frequently occurring words, the sentiment and the underlying latent themes in the responses of faculty members towards the shift from conventional to online teaching using data mining techniques. Orange data mining software was utilized to preprocess and analyze the data. VADER sentiment analysis and Latent Dirichlet Allocation Topic Modelling were used to generate the overall sentiment and the themes of the teachers’ responses. Results revealed that the most frequently occurring words in the responses of teachers were blended, online, students, teaching, teachers, learning, difficult, challenging, internet, and connectivity. Twenty-three (23) out of 37 (62%) responses were determined to have negative polarity making the general sentiment of faculty members towards the shift to online learning negative. The following themes were generated after the application of Latent Dirichlet Allocation Topic Modeling technique: unexpected shift from conventional to blended teaching, Mental and Physical Health Issues Related to the Implementation of Blended Learning, Online tools used in the conduct of Online Classes, Difficulties and Challenges in the Conduct of Blended/Online Learning, Slow Internet Connectivity as a Major Impediment in the Conduct of Online Teaching.

Index Terms—Latent dirichlet allocation, topic modelling, sentiment analysis, pandemic, teacher, COVID-19.

The author is with the Information Technology Department, Eastern Samar State University, Borongan City, Philippines (e-mail: nfcasillano@gmail.com).

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Copyright © 2021 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

General Information

  • ISSN: 2010-3689 (Online)
  • Abbreviated Title: Int. J. Inf. Educ. Technol.
  • Frequency: Monthly
  • DOI: 10.18178/IJIET
  • Editor-in-Chief: Prof. Dr. Steve Thatcher
  • Executive Editor: Ms. Nancy Y. Liu
  • Abstracting/ Indexing: Scopus (Since 2019), INSPEC (IET), UGC-CARE List (India), CNKI, EBSCO, Electronic Journals Library, Google Scholar, Crossref, etc.
  • E-mail: ijiet@ejournal.net

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