Abstract—Danmaku data from an online course contains implicit information about the students, the teacher, and the course itself. To discover the information, we design a behavior-sentiment-topic mining procedure, and apply it on the danmaku from two electronics courses on Bilibili, a popular video sharing platform in China. The procedure enables us to obtain behavior patterns, text sentiments, and hidden topics, of those danmaku comments effectively. Results show similarities and differences between the danmaku from Fundamentals of Analog Electronics and that from Fundamentals of Digital Electronics. Some interesting observations are given according to the results. For example, students tend to experience an emotional upsurge right before the end of a course, which is due to their fulfilment for completing the course. Based on the observations, we make some suggestions for students, teachers, and platforms on how to improve the learning outcomes using the results of danmaku analysis.
Index Terms—E-learning, danmaku, time-sync comments, educational data mining, learning analytics.
Linzhou Zeng, Zhibang Tan, Yu'an Xiang, and Yougang Ke are with the School of Information of Science and Engineering, Hunan Institute of Science and Technology, Yueyang, 414006, China.
Lingling Xia is with the Faculty of Education, University of Malaya, 50603, Kuala Lumpur, Malaysia.
Cite: Linzhou Zeng, Zhibang Tan, Lingling Xia, Yu'an Xiang, and Yougang Ke*, "Behavior Analytics, Sentiment Analysis, and Topic Detection of Danmaku from Online Electronics Courses on Bilibili," International Journal of Information and Education Technology vol. 13, no. 2, pp. 232-238, 2023.Copyright © 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).