Both Jon and Matt were participants at MITH’s NEH-funded topic modeling workshop 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 if I’ve missed something you’ve written that you’d like to see listed here):

• Some reflections by Thomas Padilla
• A wrap-up by Sarita Alami
• Some questions by Trevor Owens (who was not at the workshop, although many of the commenters on this post were)
• A collection of Tweets by Scott Kleinman
• And another set by Natalia Cecire, with a focus on issues of labor
• Detailed notes by Brian Croxall
• A Digital Dialogue at MITH by Lisa Rhody on the use of topic modeling in her dissertation project

We’d also like to announce a small topic modeling library and toolkit that MITH is releasing and will continue to develop over the next few months. This library is written in the Scala programming language and currently serves primarily as a lightweight wrapper for MALLET. It pulls together bits of functionality and code that we at MITH found ourselves developing for various projects with a topic modeling component, including a graduate course project on the Gothic novel and science fiction, Lisa Rhody’s work on ekphrastic poetry, Amanda Visconti‘s work on visualizing Digital Humanities Quarterly, and the  Foreign Literatures in America project.

One simple piece of functionality that we’ve found widely useful is a command-line tool that exports data from a model file generated by MALLET to a spreadsheet that can be opened in Excel or LibreOffice. While Excel is in many ways a less sophisticated data analysis platform than tools like R, it is widely used and has a relatively shallow learning curve. For example, this tool has allowed us to train a topic model and hand the results as a spreadsheet to a group of undergraduate students, who can then easily identify the documents in their corpus most strongly associated with a particular topic, or find the documents that are most similar (according to the topic model) to a particular text they are reading.

The project currently doesn’t go out of its way to insulate users from the command line, but it is designed to be easy to install and use. It relies on the Maven build tool to manage dependencies, so you can run MALLET’s topic modeling engine (with reasonable defaults) without manually installing MALLET on your machine, for example. If you’re on a Mac with OS X, you already have Maven installed, and if you run Windows or Linux the installation process of installing Maven is fairly painless and straightforward.

If you’re curious about topic modeling and are willing to roll up your sleeves and open a terminal, we’d encourage you to click the link above and try out this software. This is very much a work in progress, so let us know about features you’d like to see—either in a comment here or by creating a new issue in the GitHub repository—and we’ll do our best to get them implemented. And watch this space—in the spring we’ll be launching a sandbox environment that will allow users to run MALLET and other topic modeling tools without installing any software on their local machine.

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First of all, we’d like to encourage everyone to check out Paper Machines, a Zotero extension developed by Jo Guldi and Chris Johnson-Roberson, who will be at the workshop next week to discuss how their project uses topic modeling to provide visualizations of the distribution of topics over time in the contents of your personal Zotero library. We’ve created a Storify post recording just a few of the recent reactions to Paper Machines on Twitter.

There have been a number of interesting conversations on topic modeling in the context of humanities research on Twitter in just the last couple of weeks, including these discussions about how to evaluate and interpret topic models, how to get detailed information about word distributions out of MALLET, and how to use topic models to aid navigation of text collections. Many of the participants in these threads will be at the workshop next week, and we’re looking forward to continuing the conversations there.

We’d also like to point everyone to the public Zotero library created by project director Jennifer Guiliano, which lists papers and blog posts about topic modeling methods and humanities applications; to workshop participant David Mimno‘s comprehensive bibliography; and to the topic modeling Subreddit recently started by Matt Burton (who will also be at the workshop).

I’ve also just put together a Twitter list of all of the participants whose Twitter handles I could round up, and the complete list of speakers and attendees is available on the project website. (If you’re attending the workshop and are on Twitter but not on the list, please let me know).

Please see the project website for a more detailed description of the goals of the workshop and the schedule of presentations and discussions, as well as information about logistics for workshop participants, and be sure to follow #dhtopic on Twitter on Saturday, November 3—one week from tomorrow!

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