I’ve been collaborating with Michael Simeone of I-CHASS on strategies for visualizing topic models. Michael is using d3.js to build interactive visualizations that are much nicer than what I show below, but since this problem is probably too big for one blog post I thought I might give a quick preview.

Basically the problem is this: How do you visualize a whole topic model? It’s easy to pull out a single topic and visualize it — as a word cloud, or as a frequency distribution over time. But it’s also risky to focus on a single topic, because in LDA, the boundaries *between* topics are ontologically sketchy.

After all, LDA will create as many topics as you ask it to. If you reduce that number, topics that were separate have to fuse; if you increase it, topics have to undergo fission. So it can be misleading to make a fuss about the fact that two discourses are or aren’t “included in the same topic.” (Ben Schmidt has blogged a nice example showing where this goes astray.) Instead we need to ask whether discourses are relatively near each other in the larger model.

But visualizing the larger model is tricky. The go-to strategy for something like this in digital humanities is usually a network graph. I have some questions about that strategy, but since examples are more fun than abstract skepticism, I should start by providing an illustration. The underlying topic model here was produced by LDA on the top 10k words in 872 volume-length documents. Then I produced a correlation matrix of topics against topics. Finally I created a network in Gephi by connecting topics that correlated strongly with each other (see the notes at the end for the exact algorithm). Topics were labeled with their single most salient word, except in three cases where I changed the label manually. The size of each node is roughly log-proportional to the number of tokens in the topic; nodes are colored to reflect the genre most prominent in each topic. (Since every genre is actually represented in every topic, this is only a rough and relative characterization.)

Click through for a larger version.

Read full post here. (Originally posted November 11, 2012)