Exploring the assigned case studies, America’s Public Bible, Signs@40, and Robots Reading Vogue, in conjunction with the analyses by Goldstone (2014), Leonard (2014), and Mullen (2021), highlights the effectiveness of Voyant tools for creating dynamic text mining experiences for text analysis projects. These case studies demonstrate how Voyant can produce dynamic, interactive data visualizations for users, as in the Signs@40 project, which features a network graph representing author cocitations and an interactive, curated table of contents with commentaries from past, current, and future editors and contributors.
By applying the text analysis algorithms built into Voyant tools, I analyzed the entire corpus or specific state files of the WPA Slave Narratives project. The content tools allowed me to view word use by state and compare frequently used words between two states. I noticed that most interviews across states used some of the same words at high frequency, including “old”, “come”, and “got”, but within states, these words varied by region.
Voyant tools also enabled me to understand narrators’ words in context across the corpus. For example, the word “got” was used in interviews across the corpus in contexts referring to actions done to them as persons with limited agency.
The Voyant tool also identified distinctive words that varied in frequency due to dialect differences, topic relevance, or the interview format. By connecting frequently used words across state datasets, I gained insights into the meanings of these differences. Further, text analysis of these interviews using Voyant tools revealed a deeper understanding of how variations in word use reflected narrators’ experiences as formerly enslaved individuals.
A Voyant feature most helpful for text analysis is its ability to remove common words, or stop words, that can clutter the analysis. I was able to customize the stop word list by adding corpus stop words to Voyant’s default list, enabling me to refine my visualizations. For example, one of the most used words by narrators in Georgia appeared to be “war” as one of the top five most frequently used words generated in the Cirrus tool from the corpus was “war”. When I examined the context in which the word was used, however, it was clear that the actual word counted was the commonly used word “warn’t,” so I added “warn’t” to the stop word list to refine the distinctive words list results.
The assigned WPA Slave Narratives project answered the question of how narrators’ words used to describe their enslavement experiences and spoke to the nature of the institution of slavery in some American states over time. This project also helped answer the question of how narrators’ accounts of enslavement experiences were used and changed over time, similar to findings from the America’s Public Bible project, which explored how the Bible, as a text, was used in the public sphere and evolved over time (Guldi, 2024). As a researcher, I find this text-mining tool invaluable for analyzing corpora and making meaningful comparisons across datasets.