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 (and other product reviews), we create a pipeline for parsing reviews, analyzing their sentiment, identifying nonsense reviews, and generating human interpretable metrics of course quality.