Behavior Analytics, Sentiment Analysis, and Topic Detection of Danmaku from Online Electronics Courses on Bilibili

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.