TY - CHAP
T1 - Learning sentiment from students’ feedback for real-time interventions in classrooms
AU - Altrabsheh, Nabeela
AU - Cocea, Mihaela
AU - Fallahkhair, Sanaz
PY - 2014
Y1 - 2014
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.
U2 - 10.1007/978-3-319-11298-5_5
DO - 10.1007/978-3-319-11298-5_5
M3 - Chapter (peer-reviewed)
SN - 9783319112978
T3 - Lecture Notes in Computer Science
SP - 40
EP - 49
BT - Adaptive and intelligent systems
A2 - Bouchachia, Abdelhamid
PB - Springer
CY - Heidleberg
ER -