  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.
