  Peer review is a popular feedback mechanism in higher education that actively
engages students and provides researchers with a means to assess student
engagement. However, there is little empirical support for the durability of
peer review, particularly when using data predictive modeling to analyze
student comments. This study uses Na\"ive Bayes modeling to analyze peer review
data obtained from an undergraduate visual literacy course over five years. We
expand on the research of Friedman and Rosen and Beasley et al. by focusing on
the Na\"ive Bayes model of students' remarks. Our findings highlight the
utility of Na\"ive Bayes modeling, particularly in the analysis of student
comments based on parts of speech, where nouns emerged as the prominent
category. Additionally, when examining students' comments using the visual peer
review rubric, the lie factor emerged as the predominant factor. Comparing
Na\"ive Bayes model to Beasley's approach, we found both help instructors map
directions taken in the class, but the Na\"ive Bayes model provides a more
specific outline for forecasting with a more detailed framework for identifying
core topics within the course, enhancing the forecasting of educational
directions. Through the application of the Holdout Method and $\mathrm{k}$-fold
cross-validation with continuity correction, we have validated the model's
predictive accuracy, underscoring its effectiveness in offering deep insights
into peer review mechanisms. Our study findings suggest that using predictive
modeling to assess student comments can provide a new way to better serve the
students' classroom comments on their visual peer work. This can benefit
courses by inspiring changes to course content, reinforcement of course
content, modification of projects, or modifications to the rubric itself.
