Communications in Humanities Research
- The Open Access Proceedings Series for Conferences
Vol. 11, 31 October 2023
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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.
student loan, topic modeling, text mining, twitter
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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
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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