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… to have them for an instant in her hands both at once,
the story and its undoing…
from “Self Portrait as Hurry and Delay” [Penelope at her loom]
Located at the center of Jorie Graham’s collection The End of Beauty, “Self Portrait as Hurray and Delay” crafts a portrait of the artist, poised at a precarious moment in which thought begins to take shape.
The topic model discussed in “Topic Modeling and Figurative Language” was created with MALLET. Drawing from 4,500 English-language poems from the “Revising Ekphrasis” corpus, the model was generated using the following parameters:
mallet train-topics --input poems-seq.mallet --num-threads 2 --num-topics 60 --optimize-interval 10 --output-model poems08072012test1.model --output-doc-topics poems08072012_test1.txt --output-topic-keys poems08072012-test1keys.txt
The following table contains the number of the topic (0-59); hyper-parameter estimation; and top 20 key words most likely to be found in each topic.
Of all our literary-historical narratives it is the history of criticism itself that seems most wedded to a stodgy history-of-ideas approach — narrating change through a succession of stars or contending schools.
As this issue shows, there is no shortage of interest among humanists in using topic modeling. An entire genre of introductory posts has emerged encouraging humanists to try LDA. So many scholars in humanities departments are turning to the tool in their research that it is sometimes described as part of the digital humanities in itself.
Topic Modeling Ships
Begin by getting the data in order. (This data is available on request.)
rm(list = ls())
This step pulls in the Maury data and splits it.