This case introduces faculty and students to text analytics. Since at least 80 percent of data are unstructured, the broad dataset and accounting/business problem developed for this case provides a rich context to analyze unstructured data.1 Analyzing unstructured data via text analysis has traditionally been reserved for courses that focus on teaching computer programming languages, such as Perl, Python, R, and SQL. Although instructors may want to teach text analytics so that students can be prepared to get value out of the vast amounts of unstructured data available, this traditional approach required an instructor to trade off the instruction of necessary accounting/business topics with the instruction time required to teach the necessary computer programming language.
Fortunately, advances in self-service data analytics tools, such as Alteryx and RapidMiner, provide instructors and students with a low-code approach, which makes text analytics more accessible. Appendix A can be used to introduce faculty...
