Are Top School Students More Critical of Their Professors? Mining Comments on RateMyProfessor.com
This study provides a data-driven approach to assess teaching quality, which is useful for students and educators, but it is incremental as it applies existing methods to a new dataset.
The paper analyzed student comments on RateMyProfessor.com using Latent Dirichlet Allocation and sentiment analysis to uncover insights about student and professor characteristics, demonstrating that these reviews contain crucial information for course and university enrollment decisions.
Student reviews and comments on RateMyProfessor.com reflect realistic learning experiences of students. Such information provides a large-scale data source to examine the teaching quality of the lecturers. In this paper, we propose an in-depth analysis of these comments. First, we partition our data into different comparison groups. Next, we perform exploratory data analysis to delve into the data. Furthermore, we employ Latent Dirichlet Allocation and sentiment analysis to extract topics and understand the sentiments associated with the comments. We uncover interesting insights about the characteristics of both college students and professors. Our study proves that student reviews and comments contain crucial information and can serve as essential references for enrollment in courses and universities.