1. Computer languages:
   1. Python
   2. R
2. Specific Programs
   3. OpenCV
   4. Topic Modeling
   5. Word Embedding

benschmidt.org/medhist16

web browser: localhost:8787

Bill Lane Center for the American West: http://www.stanford.edu/group/ruralwest/cgi-bin/drupal/visualizations/us_newspapers

Textual Metadata: Correspondence Networks

Mapping the Republic of Letters

Textual Metadata: Trial Lengths

Data Mining with Criminal Intent/The Old Bailey Online

Co-citation Networks

• Kieran Healy, Philosophy Citations

• Neal Caren, Sociology Citations

• Jonathan Goodwin, Film Studies and more

More data from:

Jstor Data for Research: dfr.jstor.org

dfr.jstor.org

Ngrams: New Archaicism

Ngrams: Presses

Ngrams: 3 common words

Ngrams: 3 2044

Words the drop off between 1918-1922 and 1923-1927

• uterus

• spleen

• tuberculosis

• pulmonary

General purpose sources of digital texts

• Hathi Trust (http://www.hathitrust.org/)

• JStor Data for research (dfr.jstor.org)

• EEBO-TCP: http://www.textcreationpartnership.org/tcp-eebo/

• National Newspaper corpora (Chronicling America for the US, Trove for Australia.)
• University Librarians
  • Ask for local copies.

Sources for medical texts

Let's figure this out.

https://goo.gl/BzWKQ6

Optical Character Recognition

Ted Underwood, University of Illinois

Word Clouds

History Dissertations, Robert Townsend

Geoparsing and geotagging

Matt Wilkens, Notre Dame: "The distribution of US city-level locations, revealing a preponderance of literary–geographic occurrences in what we would now call the Northeast corridor."

Not just tokens

Texts are "multiply addressable" (Michael Witmore)

Use metadata!

Hathi Trust Bookworm:

https://bookworm.htrc.illinois.edu/

Medical Heritage Library Bookworm

Experimental Index Catalog Bookworm

State of the Union Comparisons

Naive Bayes

• Even better: Logistic regression.
• Or Support Vector Machine
• Or K-Nearest-neighbor *... There are textbooks on this stuff.

Topic Modeling

David Blei, Princeton University

Go-to software for text analysis.

• With just a web browser:
  • Cut and paste into an online environment: Voyant: voyant-tools.org
• Following some arcane instructions:
  • Topic modeling and machine learning: MALLET
  • Network Analysis: Gephi
  • Data visualization: Tableau?
  • Tutorials at ProgrammingHistorian.org
• Some programming required.
  • Cleaning and processing .txt files: Python
  • Statistical analysis: The "R" Language
  • Data visualization: R or D3 (Javascript, online)
  • Sharing libraries online: Bookworm

(reminder: this list is at benschmidt.org/medhist16)

Open Questions

• Do you (or does someone else) need to program?
• Do you need more texts than you can read?
• If this is interdisciplinary, what discipline?