Text analysis has been a popular form of computational analysis since it’s inception. Whether you support close reading, distant reading, or a healthy mixture of both, there is always something to be learned when evaluating, comparing, and considering the words used by scholars, authors, poets, and anyone in between.
Voyant Tools is a popular online source for analyzing digital texts. Any user can upload their word source and then play with the various visualizations offered by the site. All of the visualizations show the various relationships between the digitized words and can be connected and presented in unique ways. The image above shows the first page Voyant shows after analyzing the American Medical Association (AMA) Journal of Ethics, July 2018 edition. The screenshot shows the page exactly as it first appeared. I did not make any edits or refine any key-terms. This is why abbreviations like ‘dr’ are visible.
For Lab 9 of my Digital Humanities course, I evaluated the various ways to organize and then visualize data. These graphics were done using Tableau Prep and Tableau Desktop and are far from comprehensive. The dataset manipulated for these graphics came from a group called Gallup in 2019 and is titled the Self-described religious identification of Americans. This dataset is similar to the Longitudinal Religious Congregations and Membership File discussed in previous posts as it also looks at self-identified religious groups over time. Although both evaluate similar categories, they each draw the categorical lines differently, and beyond that, count category members differently (but this is an idea that I’ll explore later).
For now, it is important to understand the process of visualizing data. Once you’re the one in charge, the choices of inclusion and exclusion become quite obvious. Consider my first attempt at cleaning and visualizing the Gallup data: