Learning Sentiment from Students' Feedback for Real-Time Interventions in Classrooms

Nabeela Altrabsheh, Mihaela Cocea, Sanaz Fallahkhair

Research output: Chapter in Book/Conference proceeding with ISSN or ISBNConference contribution with ISSN or ISBN

Abstract

Knowledge about users sentiments can be used for a variety of adaptation purposes. In the case of teaching, knowledge about students sentiments can be used to address problems like confusion and boredom which affect students engagement. For this purpose, we looked at several methods that could be used for learning sentiment from students feedback. Thus, Naive Bayes, Complement Naive Bayes (CNB), Maximum Entropy and Support Vector Machine (SVM) were trained using real students' feedback. Two classifiers stand out as better at learning sentiment, with SVM resulting in the highest accuracy at 94%, followed by CNB at 84%. We also experimented with the use of the neutral class and the results indicated that, generally, classifiers perform better when the neutral class is excluded.
Original languageEnglish
Title of host publicationThird International Conference, ICAIS 2014
Place of PublicationHeidleberg
PublisherSpringer
Pages40-49
Number of pages10
Volume8779
DOIs
Publication statusPublished - 31 Dec 2014
EventThird International Conference, ICAIS 2014 - Bournemouth, UK, September 8-10, 2014
Duration: 31 Dec 2014 → …

Publication series

NameLecture Notes in Computer Science

Conference

ConferenceThird International Conference, ICAIS 2014
Period31/12/14 → …

Bibliographical note

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-11298-5_5

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    Altrabsheh, N., Cocea, M., & Fallahkhair, S. (2014). Learning Sentiment from Students' Feedback for Real-Time Interventions in Classrooms. In Third International Conference, ICAIS 2014 (Vol. 8779, pp. 40-49). (Lecture Notes in Computer Science). Springer. https://doi.org/10.1007/978-3-319-11298-5_5