Text Mining of Online Student Reviews of Postsecondary Institutions Using Large Language Models
Abstract/Contents
- Abstract
- Choosing a college for the next four years is perceived as a high-stakes and high-risk decision for students. Oftentimes, students rely on word-of-mouth (WOM) from parents, families, peers, and guidance counselors to compare college choices. As the young generation is more adapted to searching information on the internet and online communities, eWOM draws scholarly attention towards understanding how students utilize that information for college decisions. This paper leverages the innovative application of Large Language Models (LLMs) for text mining, aiming to extract and analyze topic-specific sentiments expressed by online reviews. In addition, it explores the qualitative differences in college ratings by analyzing the sentiment and lexicon of student reviews. The practical implications of this study are twofold. On one hand, it assesses the accuracy of LLMs in predicting topics and sentiments in comparison to manual coding, offering a robust framework for large-scale text analysis. On the other hand, it provides insights for prospective students and their families, facilitating more informed decision-making processes around college decisions.
Description
| Type of resource | text |
|---|---|
| Date modified | June 10, 2024 |
| Publication date | May 30, 2024 |
Creators/Contributors
| Author | Yin, Kathy |
|---|
Subjects
| Subject | college information |
|---|---|
| Subject | eWoM |
| Genre | Text |
| Genre | Capstone |
| Genre | Thesis |
| Genre | Student project report |
Bibliographic information
Access conditions
- Use and reproduction
- User agrees that, where applicable, content will not be used to identify or to otherwise infringe the privacy or confidentiality rights of individuals. Content distributed via the Stanford Digital Repository may be subject to additional license and use restrictions applied by the depositor.
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 4.0 International license (CC BY-NC).
Preferred citation
- Preferred citation
- Yin, K. (2024). Text Mining of Online Student Reviews of Postsecondary Institutions Using Large Language Models. Stanford Digital Repository. Available at https://purl.stanford.edu/zp863nj0016. https://doi.org/10.25740/zp863nj0016.
Collection
Education Data Science (EDS) Capstone Projects, Graduate School of Education
View other items in this collection in SearchWorksContact information
- Contact
- kathy.yin@stanford.edu
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