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

Nabeela Altrabsheh; Mihaela Cocea; Sanaz Fallahkhair

doi:10.1007/978-3-319-11298-5_5

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

Nabeela Altrabsheh, Mihaela Cocea, Sanaz Fallahkhair

University of Brighton

Research output: Chapter in Book/Conference proceeding with ISSN or ISBN › Conference contribution with ISSN or ISBN › peer-review

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 language	English
Title of host publication	Third International Conference, ICAIS 2014
Place of Publication	Heidleberg
Publisher	Springer
Pages	40-49
Number of pages	10
Volume	8779
DOIs	https://doi.org/10.1007/978-3-319-11298-5_5
Publication status	Published - 31 Dec 2014
Event	Third International Conference, ICAIS 2014 - Bournemouth, UK, September 8-10, 2014 Duration: 31 Dec 2014 → …

Publication series

Name	Lecture Notes in Computer Science

Conference

Conference	Third International Conference, ICAIS 2014
Period	31/12/14 → …

Bibliographical note

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

Access to Document

10.1007/978-3-319-11298-5_5Licence: Unspecified

ICAIS.pdfAccepted author manuscript, 149 KBLicence: Unspecified

Sanaz Fallahkhair
- S.Fallahkhairbrighton.acuk
- School of Arch, Tech and Eng - Principal Lecturer
- Computing and Mathematical Sciences Research Excellence Group
Person: Academic

Cite this

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title = "Learning Sentiment from Students' Feedback for Real-Time Interventions in Classrooms",

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.",

author = "Nabeela Altrabsheh and Mihaela Cocea and Sanaz Fallahkhair",

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Learning Sentiment from Students' Feedback for Real-Time Interventions in Classrooms. / Altrabsheh, Nabeela; Cocea, Mihaela; Fallahkhair, Sanaz.
Third International Conference, ICAIS 2014. Vol. 8779 Heidleberg: Springer, 2014. p. 40-49 (Lecture Notes in Computer Science).

Research output: Chapter in Book/Conference proceeding with ISSN or ISBN › Conference contribution with ISSN or ISBN › peer-review

TY - GEN

T1 - Learning Sentiment from Students' Feedback for Real-Time Interventions in Classrooms

AU - Altrabsheh, Nabeela

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AU - Fallahkhair, Sanaz

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PY - 2014/12/31

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N2 - 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.

AB - 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.

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BT - Third International Conference, ICAIS 2014

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T2 - Third International Conference, ICAIS 2014

Y2 - 31 December 2014

ER -

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

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Sanaz Fallahkhair

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