TY - GEN
T1 - Learning Sentiment from Students' Feedback for Real-Time Interventions in Classrooms
AU - Altrabsheh, Nabeela
AU - Cocea, Mihaela
AU - Fallahkhair, Sanaz
N1 - The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-11298-5_5
PY - 2014/12/31
Y1 - 2014/12/31
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 - Conference contribution with ISSN or ISBN
VL - 8779
T3 - Lecture Notes in Computer Science
SP - 40
EP - 49
BT - Third International Conference, ICAIS 2014
PB - Springer
CY - Heidleberg
T2 - Third International Conference, ICAIS 2014
Y2 - 31 December 2014
ER -
