Session 3
Oct. 2
planned
Descriptive Patterns
Represent texts as counts and TF-IDF weights, then explain what each representation makes visible.
Korean Studies · Digital Humanities
BA3 Text as Data
This six-class digital humanities strand introduces text-as-data methods for Korean Studies and concludes with an in-class hackathon. Using Orange Data Mining and prepared corpora, you will learn to organize and compare texts. The course also includes curated R exercises and regular work in GitHub.
- Dates
- Sep. 18–Nov. 13, 2026
- Class time
- Fridays, 09:15–11:00
- Location
- Digital Humanities Lab · Johan Huizinga 0.09
Course map
Six classes and one in-class project
Each class introduces one main research design, demonstrates it, and gives you time to begin the week’s assignment.
Session 1 and Session 2 guides are available now. Later guides open on the dates shown.
Session 1
Sep. 18
upcoming
Introduction to Text as Data
Define a feasible text-as-data question and create a course repository that another person can understand.
Session 2
Sep. 25
planned
Text Preprocessing
Apply and document a small set of preprocessing choices, then inspect how they change the text.
Session 4
Oct. 9
planned
Clustering and Similarity
Compare documents by vocabulary and build a hierarchical clustering. Check the groupings against the source texts.
Session 5
Oct. 16
planned
Classification and Sentiment Analysis
Apply a documented sentiment-scoring workflow and inspect where its classifications are useful or misleading.
Session 6
Oct. 30
planned
Topic Modeling
Fit and interpret a small topic model, check it against source texts, and define a feasible hackathon analysis.
Final
Nov. 13
hackathon
In-class text-as-data hackathon
Complete and verify one analysis during the four-hour class, then report the result.
How each class works
- Complete the attendance survey and answer short questions about earlier material. Devices may be used.
- Review the main research decision for the day.
- Put screens and phones away during the lecture.
- Open your laptop and follow the demonstration.
- Save the work and review the assignment checklist. Finish any remaining steps after class.
Read the complete classroom routine and electronics policy →
Assessment
Within the DH strand
The strand is 25% of the full course grade.
30%
Weekly deliverables & attendance
70%
In-class hackathon
Standard track
Learn through Orange
Orange Data Mining keeps the analytical workflow visible. The R activities provide introductory programming practice.
BA2 review track
Do it again in R
If you took BA2 Digital Korea, you may review the course. Complete every weekly assignment and the hackathon in R. Orange is not permitted.