  Machine learning enables the development of new, supplemental, and empowering
tools that can either expand existing technologies or invent new ones. In
education, space exists for a tool that supports generic student course review
formats to organize and recapitulate students' views on the pedagogical
practices to which they are exposed. Often, student opinions are gathered with
a general comment section that solicits their feelings towards their courses
without polling specifics about course contents. Herein, we show a novel
approach to summarizing and organizing students' opinions via analyzing their
sentiment towards a course as a function of the language/vocabulary used to
convey their opinions about a class and its contents. This analysis is derived
from their responses to a general comment section encountered at the end of
post-course review surveys. This analysis, accomplished with Python, LaTeX, and
Google's Natural Language API, allows for the conversion of unstructured text
data into both general and topic-specific sub-reports that convey students'
views in a unique, novel way.
