Mining (Con)texts

John Unsworth gave a talk at Harvard tonight teasingly titled "How Not to Read a Million Books: Text Mining, and Reading the Unreadable." He spoke mostly about the MONK project,a Mellon-funded collaboration that's familiar, I'm sure, to many HASTACers.MONK applies text mining techniques and visualizations to discover newdimensions to literary and historical texts.

Unsworth describedthe work of several scholars already using the MONK toolkit in theirwork. For instance, Tanya Clement, a PhD candidate in EnglishLiterature and Digital Studies, has successfully applied MONK to herresearch on Gertrude Stein's The Making of Americans, as she described in a recent article for Literary and Linguistic Computing:

"The particular reading difficulties engendered by the complicated patterns of repetition in The Making of Americansmirror those a reader might face attempting to read a large collectionof like texts at once without getting lost?likewise, it is almostimpossible to read this text in a traditional, linear manner. However, byvisualizing certain patterns and looking at the text ?from a distance?through textual analytics and visualizations, we are enabled to makereadings that were formerly inhibited. Franco Moretti has arguedthat the solution to truly incorporating a more global perspective inour critical literary practices is not to read more of the vast amountsof literature available to us, but to read it differently by employing'distant reading'. 'We know how to read texts', he writes, ?now let'slearn how not to read them' (Moretti, 2000Go, p. 57). Similarly, bylearning to read texts that have been misread 'at a distance', we arereading differently and we value different readings."

SaraSteger, a PhD candidate in English at University of Georgia, issimilarly using MONK in her study of sentimentalism innineteenth-century novels. Not only could she train the program torecognize sentimental scenes, she then was able to mine a collection oftexts for over-represented words in, for instance, Victorian deathbedscenes:

And, then, under-represented words in those same scenes:

Her results invite new research into the absence of formal expressions ofmourning ("holy," "country," "lord"), and the presence of physical and emotional closeness ("pillow," "cheek," "breath") in deathbed scenes.

I want to underscore that I think these tools do offerincredible, never-before-possible ways of looking at texts. But: Iwonder about how slippery the word "text" becomes in the phrase "textmining." MONK and similar projects focus narrowly on "text" as astring of letters than can plucked from any material context, ploppedinto another and manipulated, "mined," for meaning. Let's assume for asecond that the OCR software always works perfectly (it doesn't), andthat the scans of our target book have picked up all the paratexts,including the copyright page, advertisements, promotional blurbs, evenpage numbers. Then take that lump of letters and drop itinto a text file. What are you left with? What so-called "accidentals,"what context, has been lost in translation?

I'm reminded of Kenneth Goldsmith's book Day,in which he re-typed one day's New York Times word for word, from theupper left hand corner to the lower right hand corner, including pagenumbers and any text in advertisements. The resulting book -- a thicktome that essentially levels the dynamic space of the newspaper --might have been the newspaper . . . but definitely was not the newspaper.

Thesequestions become relevant particularly for Victorian novels, many ofthem stuffed with advertising and illustrations, or published seriallyin magazines alongside political cartoons or recipes. The Wordlescreated from deathbed scenes are fascinating and very exciting to me;but unless they're paired with some old-school bibliographic analysis,I worry that more has been elided from the text than it's worth. I alsowonder (given my own interests) how text mining would work for earlymodern books, many of which may ascribe meaning and significance to"accidentals" like italics, capitalization and typographic variation.Unsworth acknowledged that text mining should only be one tool in theresearcher's toolkit. What, then, would a combination of MONK-like textmining and bibliography would look like? How can we apply "distantreading" to texts-as-strings-of-letters, while simultaneously doing a"close reading" of texts-as-material-objects?