Article: What are students thinking and feeling? Understanding them from social data mining Journal: International Journal of Computer Applications in Technology (IJCAT) 2021 Vol.65 No.2 pp.110 - 117 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. Inderscience Publishers - linking academia, business and industry through research

Title: What are students thinking and feeling? Understanding them from social data mining

Authors: Hua Zhao; Yang Zuo; Chunming Xu; Hengzhong Li

Addresses: College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, Shandong, China ' College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, Shandong, China ' College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, Shandong, China ' College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, Shandong, China

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.

Keywords: social data mining; classification; sentiment analysis; education.

DOI: 10.1504/IJCAT.2021.114985

International Journal of Computer Applications in Technology, 2021 Vol.65 No.2, pp.110 - 117

Received: 15 Jun 2020
Accepted: 29 Jul 2020
Published online: 13 May 2021 *

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Title: What are students thinking and feeling? Understanding them from social data mining

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