• Readings for Next Series of Classes
  • For Thursday
    • Andrew Goldstone and Ted Underwood, “The Quiet Transformations of Literary Studies: What Thirteen Thousand Scholars Could Tell Us” (2014)
      • Andrew Goldstone, Topic model of 100 years of literary criticism journals (visualized in Goldstone’s Dfr-browser interface)
    • Due this Thursday, Oct. 20th: Project Concept Proposal 1 (Text Analysis Project Proposal)
      • For an example of a proposal that outlines research goals and context, see the WhatEvery1Says (WE1S) grant proposal to the Mellon Foundation written by Alan Liu (abbreviated version). See pages 4 (beginning with “WE1S adds uniquely to this broader field of research and advocacy by using digital humanities methods…”) to 7 (“Expected Audiences and Outcomes”). This is an example of proposing research directions (and questions), sketching “expected” outcomes, etc.

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    For next week (Tuesday)

    • Andrew Goldstone et al., Topic model of 40 years of the Signs journal of “Women in Culture and Society” (visualized in Goldstone’s Dfr-browser interface)
      • Helpful in learning how to work with Dfr-browser is the guide page on “Interpreting the topic model of Signs“
    • Andrew Piper, excerpt from “Topoi (Dispersion),” in Enumerations: Data and Literary Study (2019)  — read only pp. 66–75 [available on course Canvas site]
  • For next week (Thursday):
    • Background Readings in Linguistics Theory
      • Ferdinand de Saussure, Course in General Linguistics (1959)  – read pp. 114-117, 123-27.
      • J. [John] R. [Rupert] Firth, “A Synopsis of Linguistic Theory, 1930-55” (Oxford: Blackwell, 1957) – read sections III-IV (pp. 7-13) [available on course Canvas site]
    • Due next Thursday, Oct. 27th: Topic Modeling Exercise

David M. Blei, “Probabilistic Topic Models” (2013)

Imagine searching and exploring documents based on the themes that run through them. We might “zoom in” and “zoom out” to find specific or broader themes; we might look at how those themes changed through time or how they are connected to each other. Rather than finding documents through keyword search alone, we might first find the theme that we are interested in, and then examine the documents related to that theme. (77)

[Note]: Indeed calling these models “topic models” is retrospective — the topics that emerge from the inference algorithm are interpretable for almost any collection that is analyzed. The fact that these look like topics has to do with the statistical structure of observed language and how it interacts with the specific probabilistic assumptions of LDA. (78n.)

Ted Underwood, “Topic Modeling Made Just Simple Enough” (2012)

Of course, we can’t directly observe topics; in reality all we have are documents. Topic modeling is a way of extrapolating backward from a collection of documents to infer the discourses (“topics”) that could have generated them. (The notion that documents are produced by discourses rather than authors is alien to common sense, but not alien to literary theory.)

As a literary scholar, I find that I learn more from ambiguous topics than I do from straightforwardly semantic ones. When I run into a topic like “sea,” “ship,” “boat,” “shore,” “vessel,” “water,” I shrug. Yes, some books discuss sea travel more than others do. But I’m more interested in topics like this:

A topic like this one is hard to interpret. But for a literary scholar, that’s a plus. I want this technique to point me toward something I don’t yet understand, and I almost never find that the results are too ambiguous to be useful. The problematic topics are the intuitive ones — the ones that are clearly about war, or seafaring, or trade. I can’t do much with those.