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Qiang, J. P., Qian, Z. Y., Li, Y., Yuan, Y. H., & Wu, X. D. (2022). Short Text Topic Modeling Techniques, Applications, and Performance: A Survey. IEEE Transactions on Knowledge and Data Engineering, 34, 1427-1445.
https://doi.org/10.1109/TKDE.2020.2992485

has been cited by the following article:

• TITLE: A Normal University’s Disciplinary Instruction: A Close Look at Book Lending

  AUTHORS: Jiexuan Liu

  KEYWORDS: Teacher Education, Book Borrowing, Discipline, Statistics, SPSS

  JOURNAL NAME: Creative Education, Vol.14 No.8, August 30, 2023

  ABSTRACT: To investigate rules that represent the reader group’s reading preferences and support disciplinary instruction at a normal university, the researcher collects library borrowing data from 10 departments from 2013 to 2022 at Nanjing Normal University (NNU) as the research samples and divides the 10 departments into three groups based on the peak data load of borrowing. The researcher creates a number matrix to represent the recurrent number of books department readers borrowed in order to ascertain the affinity of faculties, uses SPSS modeler and SPSS to develop an Apriori model for the study of the association rules of borrowed books as well as multivariate linear equations for the impact of significant departments on various sorts of book borrowing, applies the Kruskal-Wallis H test to see if there are any differences in the borrowing of literary works among the four departments in Group II, and uses Gephi to construct co-borrowing network diagrams of the Top 10, Top 100, Top 300, and Top 500 books in a department in order to visualize repeated borrowing in an intelligible way. This study sheds light on university lecturers’ and students’ reading habits from the perspective of library borrowing statistics.