Defining metrics of success in academia is difficult. Student reviews of courses are ubiquitous in higher education, but often maligned by faculty that teach those courses who rightfully question their accuracy and utility, particularly in large enrollment introductory courses. Using largely publicly available Kaggle datasets of course reviews/student performance, we will create a pipeline for parsing these reviews, analyzing their sentiment, highlighting key factors that impact course ratings, tracking sentiment over time, and (hopefully) generating human interpretable metrics of course quality.