For the text analysis praxis assignment, I mostly explored Voyant and the JSTOR text analyzer, Constellate. Voyant was a very self-explanatory and easy to use platform. You can either upload your text or paste it directly in the text box. Then, the tool creates multiple visualizations based on that text. My first step was to choose which text to dive into. I knew I wanted it to be a simple text. Additionally, I wanted to explore something personal to me, that might reveal something pertinent to my life. To this end, I copy and pasted my two personal statements from my grad school applications into Voyant. The statistics revealed that the words used most frequently are data, children, education, and trauma. This is very much in line with my purpose going into grad school, as I am a QMSS student focusing on trauma in education. My favorite visualization was the one where my frequently used words were connected to one another. This allowed me to see not only the main themes, but how they came together to make one cohesive argument. I also noticed Voyant’s use of the word ‘Reveal’ as the button to generate the statistics. This implies there is a truth hidden within the text that must be unveiled. 

Using Constellate, I decided to explore published texts that contain the word ‘gaelic’ to stay with the theme of my previous mapping project. The visualization generated was a graph that showed the amount of texts with that keyword published over time. Furthermore, I could explore word frequencies within that sample of texts. I had a lot of fun with this feature, looking at what concepts were more and less prevalent at certain times in history. For example, both the percentage and total amount of texts published about gaelic in Ireland was much larger than those published about gaelic in Scotland. During this exploration, I found myself thinking of our readings that insist that data is never neutral and requires close reading and interpretation. For example, if you switch the summary metric from percentage to total count, an entirely different story is told. This not only requires transparency from the statistician, but attention and prior knowledge from the audience.