Slides on how to use the text mining tool, MONK.

1. MONK: An Introduction
Harriett Green, English and Digital Humanities Librarian
“Digital Humanities in Theory and Practice: Tools and Methods
for Librarians”
June 22, 2012

2. MONK, the project
http://monkproject.org
MONK, the tool
https://monk.library.illinois.edu

3. What Is MONK?
• A web-based text mining application:
https://monk.library.illinois.edu
• Contains a closed corpus of curated texts marked
up in TEI-A and Part-Of-Speech tagging
• Texts selected from public collections (i.e.,
Documenting the American South) and
proprietary (i.e., EEBO)
• Analyze texts with tools based on SEASR and
Meandre

4. How Do I Use It?
• Start at https://monk.library.illinois.edu (Don’t
forget to type the encrypted “https”!)

5. Create an Account and Log In
• Click on “Begin” button to create an account
• If you’re from a CIC institution, click on the “Begin CIC”
button in the lower box to log in with your university ID
and password
• After setting up user name and password, log
into MONK
5

6. Available Works

7. Create a Project

8. MONK Workbench
“Define Worksets” to build text corpus

9. Create Worksets
Search for texts to create a workset

10. Create Worksets
Select texts and click Save to create a Workset

11. View Texts

12. Select Toolsets
• Use tools based on SEASR and Meandre
(http://seasr.org)
• Compare Worksets: Use Dunning’s Log
Likelihood or TF/IDF comparisons to analyze
two worksets against each other
• Classification: Run Naïve Bayes analysis and
can also produce Decisions Trees

13. Select Toolsets

14. Run Analysis!

15. You Try It!
https://monk.library.illinois.edu

16. MONK in a Nutshell
• Read the MONK Tutorials:
https://monk.library.illinois.edu/cic/public/tut
orial/gettingStarted/gettingStarted.html
• A great tool for learning about text mining
• For technical help, contact:
monkquestions@library.illinois.edu

17. QUESTIONS?
Thank you for your time!
Harriett Green
green19@illinois.edu
Twitter: @greenharr