Are Top School Students More Critical of Their Professors? Mining Comments on RateMyProfessor.com

TL;DR

This study analyzes student comments on RateMyProfessor.com using topic modeling and sentiment analysis to understand perceptions of teaching quality and student-professor dynamics.

Contribution

It introduces a comprehensive methodology combining data partitioning, exploratory analysis, LDA, and sentiment analysis to examine student reviews at scale.

Findings

Students tend to be more critical of professors they rate poorly.

Sentiment analysis reveals distinct emotional patterns in reviews.

Comments provide valuable insights for university enrollment decisions.

Abstract

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.