Using Text Analysis to Discover Work in JSTOR

JSTOR have just announced the JSTOR Labs Text Analyzer, a clever tool--still in Beta--that will analyze any document you upload (or text that you copy and paste) and find suggested matches in the JSTOR archives. It’s an interesting proposition--if you click that link on a phone, you can even take a picture of text and the Analyzer will process that.

You can find out more about how it works at this link, but I thought it would be fun to run it through a paper I published a while back. The paper was about the sense of time in psychoanalysis”, and I pasted the text into the Text Analyzer.

Here’s what I got:

On the one hand, this does look like a reasonable set of topics for an essay on Lacan. On the other hand, it does look a little like a generic list of topics associated with psychoanalysis.

JSTOR says that it compares the text to a list of 40,000 topics and a set of human-curated rules. As I look at this list of terms, I worry a little that none of the words I’d say are most important (interpretation, deferred action, time, temporal, etc). Meanwhile, words that get used *once*, in clearly attributive ways, such as “castration,” are identified as key topics. Likewise, the “Rotary Club” is posited as a relevant organization, which is a little grandiose for the joke I made.

The list of articles is decently relevant, but probably not enough for me to want to immediately download them as spot-on to my interests. However, this is dependent on the “prioritized terms,” so if I selected different ones this would improve.

The text analyzer is an interesting way to think about discovery in the scholarly databases we’re already familiar with, and the lab invites feedback on ways to improve its results. Why not give it a try?

Photo “Project 365 #365: 311209 The Prophecy Fulfilled” by Flickr user Pete / Public Domain