Perhaps this has occurred to others, but I was thinking this morning of the word object and the many other words it shares -ject with: project, deject, eject, reject, inject, interject … perhaps you can think of others. Like “object,” most of these words serve as both noun and verb (sometimes as adj.). In most of the etymologies, -ject comes from the Latin jacĕre meaning to throw. In the case of object, the inherited meaning is to throw something before the mind or senses. I suppose the etymology, in this case, could be viewed as unfortunate for OOO, which clearly presents objects as mind-independent, and certainly the evolved and contemporary understanding of object is mind-independent, though even being “objective” still requires a mind.

This is the rough text of a short talk I am scheduled to deliver at a symposium on ‘Future Directions in Book History’  at Cambrdige on the 24th of November 2011.

I am on the programme as talking briefly about the ‘Old Bailey Online and other resources’ (by which I assume is meant London Lives, Connected Histories, and Locating London’s Past, and the other websites I have helped to create of the last ten or twelve years).  But I am afraid I have no interest whatsoever in discussing the Old Bailey or the other websites.

In this next installment of our Digital Practitioner Series we’re talking to Mark Sample, Assistant Professor in the Department of English at George Mason University and a faculty affiliate at the Center for History and New Media.

Our interview was prompted by a piece he created (through the “publishing division” of his blog, samplereality.com) called Hacking the Accident. Using the French Oulipo group’s N+7 technique (in which every noun in a text is replaced with the seventh one following it in the dictionary) Sample took advantage of Hacking the Academy’s BY-NC Creative Commons License to produce a document that offers, in his words, “disruptive juxtapositions, startling evocations, and unexpected revelations that ruthless application of the algorithm draws out from the original work.”

Last week I presented at the Great Lakes College Association’s New Directions workshop on digital humanities (DH), where I tried to answer the question “Why the digital humanities?” But I discovered that an equally important question is “How do you do the digital humanities”?  Although participants seemed to be excited about the potential of digital humanities, some weren’t sure how to get started and where to go for support and training.

Building on the slides I presented at the workshop, I’d like to offer some ideas for how a newcomer might get acquainted with the community and dive into DH work.

(The following talk was given for the 6th Annual Nebraska Digital Workshop.)

I’m going to talk this afternoon about a central paradox of doing digital humanities–what Jerome Mcgann, one of the leading scholars of electronic texts, calls the problem of imagining what you don’t know.

In Digital Humanities, what we think we will build and what we build are often quite different, and unexpectedly so. It’s this radical disjuncture that offers us both opportunities and challenges…. What we really are asking today is how does scholarly practice change with digital humanities? Or how do we do humanities in the digital age?

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[It] wasn’t until the advent of Big Data in the 2000s and the rebranding of Humanities Computing as the “Digital Humanities” that it became the subject of moral panic in the broader humanities.

The literature of this moral panic is an interesting cultural phenomenon that deserves closer study…. We can use the methods of the Digital Humanities to characterise and evaluate this literature. Doing so will create a test of the Digital Humanities that has bearing on the very claims against them by critics from the broader humanities that this literature contains. I propose a very specific approach to this evaluation.

This visualization explores the ups and downs of the Bible narrative, using sentiment analysis to quantify when positive and negative events are happening:Bible sentiment analysis

Methodology

Sentiment analysis involves algorithmically determining if a piece of text is positive (“I like cheese”) or negative (“I hate cheese”). Think of it as Kurt Vonnegut’s story shapes backed by quantitative data.

I ran the Viralheat Sentiment API over several Bible translations to produce a composite sentiment average for each verse. Strictly speaking, the Viralheat API only returns a probability that the given text is positive or negative, not the intensity of the sentiment. For this purpose, however, probability works as a decent proxy for intensity.

Right now Latent Semantic Analysis is the analytical tool I’m finding most useful. By measuring the strength of association between words or groups of words, LSA allows a literary historian to map themes, discourses, and varieties of diction in a given period. This approach, more than any other I’ve tried, turns up leads that are useful for me as a literary scholar. But when I talk to other people in digital humanities, I rarely hear enthusiasm for it. Why doesn’t LSA get more love? I see three reasons.

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