Communications in Humanities Research ISSN: 2753-7072
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Research Article

Student Loan: Topic Modelling with Twitter Data

Pinrun SuDimensions International School* Tianran WangUWC changshu Yichen PanThe Winchendon School

* Corresponding author: [email protected]

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
Published: October 31, 2023
DOI: 10.54254/2753-7064/11/20231370
Volume: CHR Vol.11
pp. 52-58
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References

  1. Office of US Department of Education. (n.d.). Federal student loans for college or career school are an investment in your future. Federal Student Aid. Retrieved April 15, 2023, from https://studentaid.gov/understand-aid/types/loans
  2. Canché, M. S. G. (2023). Machine-driven classification of open-ended responses (MDCOR): An analytic framework and no-code, free software application to classify longitudinal and cross-sectional text responses in survey and social media research. Expert Systems with Applications, 215, 119265.
  3. IQVIA company. (n.d.). What is text mining, text analytics, and Natural Language Processing? What is Text Mining, Text Analytics and Natural Language Processing? Linguamatics. Retrieved April 15, 2023, from https://www.linguamatics.com/what-text-mining-text-analytics-and-natural-language-processing
  4. Robinson, J. S. and D. (n.d.). 6 topic modeling: Text mining with R. 6 Topic modeling | Text Mining with R. Retrieved April 15, 2023, from https://www.tidytextmining.com/topicmodeling.html
  5. Phat Jotikabukkana. (n.d.). Social media text classification by enhancing well-formed text trained ... Retrieved April 14, 2023, from https://www.researchgate.net/publication/316030904_Social_Media_Text_Classification_by_Enhancing_Well-Formed_Text_Trained_Model