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Text Analysis Reading Group Syllabus

Text Analysis Reading Group – Spring 2017

Every other Friday: 1/13, 1/27, 2/10, 2/24, 3/10, 3/24, 4/14, 4/28, 5/12
2:00-3:00pm
Mudd Conference Room (3rd Floor Mudd, next to Keck 2)

Meeting 1 — January 13:

 

Meeting 2 — January 27:

 

Meeting 3 — February 10

  • Julia Flanders and Fotis Jannidis, “Data Modeling,” in A New Companion to the Digital Humanities, ed. Susan Schreibman, Ray Siemens, and John Unsworth (Wiley Blackwell, 2016), 229–37.
  • Stéfan Sinclair and Geoffrey Rockwell, “Text Analysis and Visualization: Making Meaning Count,” in A New Companion to the Digital Humanities, ed. Susan Schreibman, Ray Siemens, and John Unsworth (Wiley Blackwell, 2016), 274–90.
  • OPTIONAL – Skill Development:
    • R: Arnold and Tilton, Humanities Data in R, chs. 9-10.
    • Jure Leskovec, Anand Rajaraman, and Jeff Ullman, Mining of Massive Datasets, 2nd ed. (Cambridge University Press, 2014), http://www.mmds.org/, ch. 1.
    • Python: Exploratory Programming for the Arts and Humanities, Chapters 5-6.

 

Meeting 4 — February 24

  • Shawn Graham, Ian Milligan, and Scott Weingart, Exploring Big Historical Data: The Historian’s Macroscope (Imperial College Press, 2015), ch. 3. [You can request through ILL]
  • Michael Witmore, “Text: A Massively Addressable Object,” in Debates in the Digital Humanities 2012 (University of Minnesota Press, 2012), http://dhdebates.gc.cuny.edu/debates/text/28.
  • OPTIONAL – Skill Development:
    • R: Jockers, Text Analysis, chs. 11–12.
    • Python: Exploratory Programming for the Arts and Humanities, Chapter 7.

 

Meeting 5 — March 10

  • Matthew L. Jockers and Ted Underwood, “Text-Mining the Humanities,” in A New Companion to the Digital Humanities, ed. Susan Schreibman, Ray Siemens, and John Unsworth (Wiley Blackwell, 2016), 291–306.
  • D. Sculley and Bradley M. Pasanek, “Meaning and Mining: The Impact of Implicit Assumptions in Data Mining for the Humanities,” Literary and Linguistic Computing 23, no. 4 (2008): 409–424, http://llc.oxfordjournals.org/content/23/4/409.short.
  • OPTIONAL – Skill Development:
    • R: Gareth James et al., An Introduction to Statistical Learning with Applications in R (Springer, 2013), chs. 2, 3, 10.
    • Leskovec, Rajaraman, and Ullman, Mining of Massive Datasets, ch. 3. http://www.mmds.org/
    • Python: Exploratory Programming for the Arts and Humanities, Chapter 10.

 

Meeting 6 — March 24

  • Ryan Cordell, “Reprinting, Circulation, and the Network Author in Antebellum Newspapers,” American Literary History 27, no. 3 (September 1, 2015): 417–445, doi:10.1093/alh/ajv028.
  • David A. Smith, Ryan Cordell, and Abby Mullen, “Computational Methods for Uncovering Reprinted Texts in Antebellum Newspapers,” American Literary History 27, no. 3 (September 1, 2015): E1–E15, doi:10.1093/alh/ajv029.
  • OPTIONAL – Skill Development:
    • Leskovec, Rajaraman, and Ullman, Mining of Massive Datasets, chs. 3 & 7. http://www.mmds.org/
    • Python: Exploratory Programming for the Arts and Humanities, Chapter 11.

 

Meeting 7 — April 14 (Topic Modeling)

Meeting 8 — April 28 (Topic Modeling)

  • Graham, Milligan, and Weingart, Exploring Big Historical Data, ch. 4.
  • David J. Newman and Sharon Block, “Probabilistic Topic Decomposition of an Eighteenth-Century American Newspaper,” Journal of the American Society for Information Science and Technology 57, no. 6 (2006): 753–767, http://onlinelibrary.wiley.com/doi/10.1002/asi.20342/full.
  • OPTIONAL – Skill Development:

 

Meeting 9 — May 12

 

Most of the content for this reading group is based on Lincoln Mullen’s “Text Analysis for Historians” class at George Mason University. See http://lincolnmullen.com/courses/text-analysis.2016/ for his full syllabus.

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