Mining Text for Meaning: Basics of Quantitative Text Analysis

Course content

Our contemporary, increasingly digital societies generate vast amounts of textual data that provide a rich source for sociological research. The scale of these novel data however poses a challenge to the approaches sociologists traditionally use to study texts. In response, automated methods of text analysis are becoming increasingly popular and the command of these methods a valuable skill in academic environments as well as on the private industry job market.


This course introduces students to quantitative text analysis, reviews selected methods falling within this category of approaches, and illustrates their implementation in the statistical programming language R. Students will learn about the origins of quantitative approaches to studying text and how they complement traditional, qualitative methodologies.  Using recent peer-reviewed publications students will gain an understanding of how these methodological approaches can be used to answer sociological questions and, in hands-on lab session, students will learn to implement selected techniques in R.


After successful participation, students will be comfortable reading current sociological research using quantitative text analysis, have an understanding of the landscape of tools used within the literature, and will have gained experience with their implementation in R.


Bachelor elective course

From Autumn 2022 the course is also offered to students at:

- Bachelor Programmes in Anthropology

Enrolled students can register the course directly through the Selfservice a KUnet without a preapproval.
Please contact the study administration at each programme for questions regarding registration.

Learning outcome

On successful completion of the course, student will be able to:


  • account for the need to apply novel methodologies to large-scale text data
  • identify methods of quantitative text analysis suited to answer sociological question of large-scale text data



  • work with R in particular as it relates to quantitative text analysis
  • evaluate and put into perspective the benefits and complementarities of quantitative text analysis with traditional forms of text analysis



  • plan sociological studies that leverage the potential of modern large-scale text data specialize in cutting-edge methodologies in quantitative text analysis

- Lectures
- Class discussions
- Applied R lab sessions

The course will use the book “Text Mining: A Guidebook for the Social Sciences” (Ignatow and Mihalcea 2017), peer-reviewed journal articles, and open-source online resources.

Students should be comfortable with using R and RStudio. Students should either have attended an Introduction to R class or complete a seven-session crash course provided at the beginning of the class.

Continuous feedback during the course
Peer feedback (Students give each other feedback)
7,5 ECTS
Type of assessment
Portfolio, -
Type of assessment details
Individual or group. A portfolio assignment is defined as a series of short assignments during the course that address one or more set questions and feedback is offered during the course. All of the assignments are submitted together for assessment at the end of the course. The portfolio assignments must be no longer than 10 pages. For group assignments, an extra 5 pages is added per additional student. Further details for this exam form can be found in the Curriculum and in the General Guide to Examinations at KUnet.
All aids allowed
Marking scale
7-point grading scale
Censorship form
No external censorship
Criteria for exam assessment

Please see the learning outcome.

  • Category
  • Hours
  • Lectures
  • 42
  • Preparation
  • 54
  • Project work
  • 35
  • Exam Preparation
  • 75
  • English
  • 206


Course number
7,5 ECTS
Programme level
Bachelor choice

1 semester

See timetable
Vejl. 40 personer.
Department of Sociology, Study Council
Contracting department
  • Department of Sociology
  • Department of Anthropology
Contracting faculty
  • Faculty of Social Sciences
Course Coordinator
  • Friedolin Merhout   (8-70776f7c72797f7e4a7d796d38757f386e75)

Friedolin Merhout, e-mail:

Saved on the 27-01-2023

Are you BA- or KA-student?

Are you bachelor- or kandidat-student, then find the course in the course catalog for students:

Courseinformation of students