DHQ: Digital Humanities Quarterly
2009
Volume 3 Number 2
Volume 3 Number 2
Text Minding: “A Response to Gender, Race, and Nationality in Black Drama, 1850-2000: Mining Differences in Language Use in Authors and their Characters”
Abstract
A response to the Data Mining cluster, exploring the role of machine learning in textual study.
Head of the first division
Should he find his way to this special issue of DHQ,
Sven Birkerts, author of The Gutenberg Elegies, would
likely add machine learning to his cataract of digital distractions besetting
literary reading in the electronic age. He would agree with the authors of “Gender, Race, and Nationality in Black Drama, 1850-2000,” I
presume, only when they conclude their discussion of the performance of algorithms in
generating feature lists that successfully distinguish American and non-American
playwrights in the Black Drama database, and anticipate the objection, “that the task itself is a trivial case, attempting to
confirm a distinction that is all too obvious to be of significant literary
interest.” The depth of literary meaning, Birkerts would assert, cannot be
mined by machines trained for data. I don’t share Birkerts’ vision that literary
reading and new media technologies are mutually exclusive. He comes to mind upon
reading these experiments in text mining, rather, as a way to illuminate what I think
the significant literary interest of machine learning can and should be for humanists
and literary critics. That interest, to put it simply before elaborating a bit
further in this response, is a renewed and more robust understanding of the
textuality, the principal object (if not subject) of our studies, that many critics
(and print-centric critics like Birkerts among them) too frequently take for
granted.
The basis for such renewed critical potential is evident when the authors frame their
experiments in machine learning as a complement, rather than supplement, to
traditional text analysis. In summarizing the performance of text mining with regard
to gender classification of characters within the drama database, the authors
emphasize this complementarity in asserting, “The
degree to which these lists reveal true differences among black American male and
female authors is a matter for discussion. The important thing is that the mining
algorithm gives fuel to the discussion and serves as a starting point for closer
textual study.” Machine learning, on this view, is a tool for closer
textual study, a staple of literary criticism; this tool is particularly rich in its
potential to identify meaningful patterns across increasingly greater amounts (and
distances) of texts. The authors are justifiably circumspect in worrying about the
ways in which such a computational tool may distort the meanings of literary texts or
offer an analysis at too great a distance from such texts. Most interesting, to me,
is the worry that the binary logic necessary for computation may reveal binary
thinking in the texts, but with the result of unduly privileging the stereotypical
force of such patterns.
I offer to this concern a three-fold response. First, we should worry such things;
but this concern, of course, is not limited to digital tools of analysis. As Jerome
McGann reminds literary critics in Radiant Textuality,
all of our critical tools of analysis are and always have been “prostheses for acting at a distance,” the same “distance that makes reflection
possible”
[McGann 2001, 103]. Second, in view of this understanding of interpretation as a dynamic between
tools and texts, always oscillating between method and meaning, I would suggest that
the self-awareness of this critical problem made evident in “Mining Differences” is not just a credit to the article; this greater
attention to the mediations of critical method is enhanced by the mediation of the
machine. The point is that the texts we study, whatever the substrate, are already
mediating technologies, compilations of linguistic and printing and picture-making
machines. Textual technologies such as the machine learning and text mining software
at issue here, can and should, if used thoughtfully, offer insight into the
technologies of the texts we study. Katherine Hayles, emphasizing the need for
literary critics to pursue “media specific
analysis,” argues that digital media give us the opportunity “to see print with new eyes”
[Hayles 2002, 33]. Birkerts could certainly take note; despite his title and his defense of the
book, there is very little Gutenberg in his de-materialized conception of
reading.
This closer understanding of text technology, it seems to me, might begin to address
the concern for binary opposition raised by the authors though not interrogated
further in the article. That limitation of the machine may well be the source of
literary insight. In his Figures in Black, the African
American literary theorist and scholar Henry Louis Gates, Jr. offers a compelling
analysis of how Frederick Douglass employs binary opposition in his first slave
narrative as a complex rhetorical device aimed at undermining, by way of revealing,
the binarity of language that slavery employs in its ontology of slaves versus men.
The critical insight is that the arbitrary and binary mechanics of language as such
can be appropriated and used differently by the author who knows how to operate and
deconstruct the literacy machine. Douglass thus mines the language of slavery and
race in his narratives in order to reveal a mind within the machine. The lesson is
that how we read and write does indeed determine what we read. For Douglass, the
interaction of medium and meaning drives the revisionary potential of his narrative;
we see the black author in print with new eyes. I see a similar insight into the
relation between ontology and textual technology suggested in “Mining Eighteenth Century Ontologies,” where the knowledge discovery of
machine learning locates its Enlightenment type in the knowledge discovery, ancestral
hypertext of the Encyclopedie. We shouldn’t be
surprised, I suspect, to find similar insights regarding the ontologies of race and
gender in the texts of black drama — a genre, after all, whose textuality is
inherently and complexly multimedia. Surprised or not, the suggestive patterning of
textuality that machine learning can reveal, combined with the critical
self-awareness that the humanities seeks, represents a significant means for this
critical discussion to move forward.
Works Cited
Birkerts 1994
Birkerts, Sven. The Gutenberg Elegies: The Fate of Reading in an Electronic Age. New York: Fawcett, 1994.
Gates 1987
Gates Jr., Henry Louis. Figures in Black: Words, Signs, and the 'Racial' Self. New York: Oxford University Press, 1987.
Hayles 2002
Hayles, N. Katherine. Writing Machines. Cambridge, MA: MIT Press, 2002.
McGann 2001
McGann, Jerome. Radiant Textuality: Literature After the World Wide Web. New York: Palgrave Macmillan, 2001.