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Student Loan: Topic Modelling with Twitter Data
- 1 Dimensions International School
- 2 UWC changshu
- 3 The Winchendon School
* Author to whom correspondence should be addressed.
Abstract
The study is about citizens’ opinions on student loans by analyzing Twitter reactions to Biden’s student loan cancellation project using the machine-driven classification of open-ended response (MDCOR) and found it saved research time, increased efficiency, and ensured authenticity and objectivity of data. After putting data into the application, we found that using five analysis topics is appropriate. The topic’s content can be predicted by seeking the relevant word for each case. The analysis of five issues related to student loans shows mixed opinions about the impact of loan forgiveness, with some key terms such as “predatory” and “donation” being significant. At the same time, some topics are not directly related to the issue.
Keywords
student loan, topic modeling, text mining, twitter
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Cite this article
Su,P.;Wang,T.;Pan,Y. (2023). Student Loan: Topic Modelling with Twitter Data. Communications in Humanities Research,11,52-58.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 4th International Conference on Educational Innovation and Philosophical Inquiries
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