So, how does it work?
You simply drag and drop a file– their demo file is an article named “Retelling the American West in the Museum” –, copy and paste text, or select a file from your computer and input it into the interface. What you drag and drop does not, necessarily, have to be an academic article. In fact, after inputting a relatively benign image for this blog, the Text Analyzer gave me remarkably useful results, relating to blogging and learning, the digital humanities and libraries.
After you drop your file into JSTOR, your analysis is broken down into terms. These terms are further broken down into topics, people, locations, and organizations. JSTOR deems which terms it believes are the most important and prioritizes them, and even gives specific weight to the most important terms. However, you can customize all of these options by choosing words from the identified terms to become prioritized terms, adding or deleting prioritized terms, and changing the weight of prioritized terms. For example, here are the automatic terms and results from the demo article:
However, I’m going to remove article’s author from being a prioritized term, add Native Americans and Brazilian art to the prioritized terms, and change the weight of these terms so that the latter two are the most important. This is how my terms and results list will look:
As you can see, the results completely changed!
While the JSTOR Text Analyzer doesn’t necessarily function in ways similar to other text analyzers, its ability to find key terms will help you not only find articles on JSTOR, but use those terms in other databases. Further, it can help you think strategically about search strategies on JSTOR, and see which search terms yield (perhaps unexpectedly) the most useful results for you. So while the JSTOR Text Analyzer is still in beta, it has the potential to be an incredibly useful tool for researchers, and we’re excited to see where it goes from here!