This project traces it’s roots back to my time as an undergraduate at Brigham Young University. In completing a minor in History, I was required to take a “professionalism” class. The class I took was labelled “History 200: Historian’s Craft.” It was an introductory course into how a historian operates. The coursework included learning about primary, secondary and tertiary sources, Chicago style citations, and the basics of historiography. The coursework was supplemental to the final research paper that each student worked on throughout the course. My paper focused around two collections of Revolutionary era sermons. I wanted to do a comparative analysis to highlight commonalities in their rhetoric. The paper found that similarities existed in their use of scriptures, themes, and imagery.
Returning to these collections years later, I wanted to know what digital methods could add to my analysis of the sermons. Text mining looks at word quantities and placement throughout a corpus of material. In my close reading (reading through each document on my own) I engaged the document with preconceived notions and interested. This “bias” directed my research towards certain types of conclusions. If I did not pay attention to a specific word or phrase, then I could have overlooked an important theme or idea. Distant reading mitigates this by feeding the corpus of materials into an algorithm that provides the end user with various statistics and visualizations. I can now see how often each words is employed and in what context that word is used.
I hope to confirm the conclusions I came to in my undergraduate paper. I am particularly interested in identifying the scriptures used by each preacher, to see if their are similarities or not among the corpus.
