Compare Tools: Voyant, CartoDB, Palladio

The opportunities created by new web-based free open source technologies in Humanities have been significant in re-examining, interpreting, and relating information from literary, cultural, social, political, or historical data. As we worked with the Library of Congress’ SWA Slave Narratives dataset, it was a journey to discover the variety of ways to visualize the intricate elements and connections within by using three of the visualization tools.


Voyant is a text mining tool that manipulates text through a range of analysis, such as cirrus, graphs, context, and verbal patterns reveal determining elements of the given data. To approach text through word count or patterns is to break it down to a level that we could not accomplish easily on our own. I feel that this tool brings out the other human side of text that reveal words and meaning through numbers.


CartoDB sets humanists off on a different journey. It pulls the data from the set and spreads it out on a map. It layers information and over laps the data to show a geographic relevance in the text. The geography of the interviews in the slave narratives provided a new narrative that grew out of the reality of the locations.


Palladio seemed the easiest to work with as far as the interface concerned. It provides a map of such informational detail that is probably the most difficult to gather by hand. It is an excellent tool to analyze relationships between people, locations, events, and ideas. These connections are illuminating relationships that have the possibility to create a new world of understanding about something we thought we knew. For instance, Palladio paints a busy visual of the relationships in the Jazz World, manages to make sense of the communication around The Letters of the Republic, and gives layers of insight into the slave narratives.

In comparing these three digital tools, in light of the slave narratives, I’d prefer to use Palladio. It seems that the network analysis of those narratives provides a deeper insight. However, there is no reason not use all three and compare the results and the questions each tool was able to bring up or answer. Although the nature of the project and the type of text/dataset and desired results may narrow the tools down to text mining or mapping there is no reason not use them interchangeably. Overall, these tools are able do something we humans would or could not easily produce, thus the possibilities are endless.

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