{"id":9907,"date":"2012-12-18T13:43:52","date_gmt":"2012-12-18T18:43:52","guid":{"rendered":"http:\/\/mith.umd.edu\/?p=9907"},"modified":"2020-10-08T16:00:49","modified_gmt":"2020-10-08T20:00:49","slug":"topic-modeling-round-up-and-some-new-software","status":"publish","type":"post","link":"https:\/\/mith.umd.edu\/topic-modeling-round-up-and-some-new-software\/","title":{"rendered":"Topic Modeling: New Software and a Wrap-up of our NEH-Sponsored Workshop"},"content":{"rendered":"

Topic modeling in (and on) the humanities has been the subject of a number of blog posts and online conversations over the past few weeks, including this article<\/a> by Andrew Goldstone<\/a> and Ted Underwood<\/a>, which provides a very clear introduction to the method and outlines a set of experiments on PMLA<\/a>, and a series of posts<\/a> by Jon Goodwin<\/a> that walk through experiments on texts from JSTOR’s Data for Research<\/a> in useful detail. The comments on these blog posts are well worth reading, along with the parallel discussions on Twitter, such as these responses<\/a> to a recent question by Matt Burton<\/a> about appropriate techniques for measuring the similarity of documents or topics.<\/p>\n

Both Jon and Matt were participants at MITH’s NEH<\/a>-funded topic modeling workshop<\/a> last month, and Jennifer Guiliano and I would like to thank all of the speakers and attendees once more, and to point again to some of the many follow-up blog posts and other documents that came out of the workshop (please comment or contact me<\/a> if I’ve missed something you’ve written that you’d like to see listed here):<\/p>\n