What are students thinking and feeling? Understanding them from social data mining Online publication date: Thu, 13-May-2021
by Hua Zhao; Yang Zuo; Chunming Xu; Hengzhong Li
International Journal of Computer Applications in Technology (IJCAT), Vol. 65, No. 2, 2021
Abstract: Students' digital footprints on social media shed light into their personal experiences. Mining results of these social data are useful for educators to understand students' mood swings and provide corresponding help. But due to the sharp increase of social data amounts, analysing these data manually is impossible. In this paper, we focus on Chinese college students, and explore a method to better understand them based on social data mining. The method firstly collects the social data related to students, creates a hierarchy category system based on data contents analysis; secondly, proposes a simple but effective multi-class classification method to classify the data into several concern categories; finally, carries out the sentiment analysis of each concern, and then looks deep into their emotion evolutionary process. Experimental results show that postgraduate entrance exam, final exam and other professional certificate exams are three prominent concerns of students, and they express worry about them.
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