TY - GEN
T1 - Mining student feedback to improve the quality of higher education through multi label classification, sentiment analysis, and trend topic
AU - Hariyani, Calandra Alencia
AU - Hidayanto, Achmad Nizar
AU - Fitriah, Nur
AU - Abidin, Zaenal
AU - Wati, Theresia
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/11
Y1 - 2019/11
N2 - This research carried out the label aspect classification, sentiment analysis, and topic trends on the Open-Ended Question (OEQ) section for Student Feedback Questionnaire (SFQ). Multi-Class aspect label classification for SFQ will choose the best classification model by comparing the results of the evaluation of accuracy, precision, recall, and Flscore for each feature combination and comparison of four classification algorithms namely Decision Tree (DT), Naive Bayes (NB), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM). The results of this research are Classification Techniques using a combination of features of TFIDF, Unigranb and Bigram with the SVM algorithm which is the best Multi-Class classification model for labeling SFQ aspects. In addition, the SentiStrenghtID algorithm used to get sentiments and also the LDA (Latent Dirichlet Allocation) used to get annual topic trends on each survey aspect label. The findings can help Higher Education to support decision making in taking proactive actions towards improvement for self-evaluation and quality.
AB - This research carried out the label aspect classification, sentiment analysis, and topic trends on the Open-Ended Question (OEQ) section for Student Feedback Questionnaire (SFQ). Multi-Class aspect label classification for SFQ will choose the best classification model by comparing the results of the evaluation of accuracy, precision, recall, and Flscore for each feature combination and comparison of four classification algorithms namely Decision Tree (DT), Naive Bayes (NB), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM). The results of this research are Classification Techniques using a combination of features of TFIDF, Unigranb and Bigram with the SVM algorithm which is the best Multi-Class classification model for labeling SFQ aspects. In addition, the SentiStrenghtID algorithm used to get sentiments and also the LDA (Latent Dirichlet Allocation) used to get annual topic trends on each survey aspect label. The findings can help Higher Education to support decision making in taking proactive actions towards improvement for self-evaluation and quality.
KW - Classification
KW - Education Data Mining
KW - Higher Education
KW - Multi Label Classification
KW - Sentiment Analysis
KW - Student Feedback
KW - Survey
KW - Trend Topic
UR - http://www.scopus.com/inward/record.url?scp=85083436718&partnerID=8YFLogxK
U2 - 10.1109/ICITISEE48480.2019.9003818
DO - 10.1109/ICITISEE48480.2019.9003818
M3 - Conference contribution
AN - SCOPUS:85083436718
T3 - 2019 4th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2019
SP - 359
EP - 364
BT - 2019 4th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2019
Y2 - 20 November 2019 through 21 November 2019
ER -