Too long for Twitter, a pointer to a new article:

• Bamman, David, Ted Underwood, and Noah A. Smith, “A Bayesian Mixed Effects Model of Literary Character” Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (2014): 370-79.
  NB. The link here is to a synopsis of the work and related info; you’ll want the authors’ PDF for details.

The new work is related to Bamman, O’Connor, and Smith’s “Learning Latent Personas of Film Characters” (ACL 2013; PDF), which modeled character types in Wikipedia film summaries. I mention the new piece here mostly because it’s cool, but also because it addresses the biggest issue that came up in my grad seminar when we discussed the film personas work, namely the confounding influence of plot summaries. Isn’t it the case, my students wanted to know, that what you might be finding in the Wikipedia data is a set of conventions about describing and summarizing films, rather than (or, much more likely, in addition to) something about film characterization proper? And, given that Wikipedia has pretty strong gender­/race­/class­/age­/nationality­/etc.­/etc./etc. biases in its authorship, doesn’t that limit what you can infer about the underlying film narratives? Wouldn’t you, in short, really rather work with the films themselves (whether as scripts or, in some ideal world, as full media objects)?

The new paper is an important step in that direction. It’s based on a corpus of 15,000+ eighteenth- and nineteenth-century novels (via the HathiTrust corpus), from which the authors have inferred arbitrary numbers of character types (what they call “personas”). For details of the (very elegant and generalizable) method, see the paper. Note in particular that they’ve modeled author identity as an explicit parameter and that it would be relatively easy to do the same thing with date of publication, author nationality, gender, narrative point of view, and so on.

The new paper finds that the author-effects model — as expected — performs especially well in discriminating character types within a single author’s works, though less well than the older method (which doesn’t control for author effects) in discriminating characters between authors. Neither method does especially well on the most difficult cases, differentiating similar character types in historically divergent texts.

Anyway, nifty work with a lot of promise for future development.