More Than Words: Introduction to 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.

Education

MA Research Methodology and Practice (MSc Curriculum 2015)

Course package (MSc 2015):

Welfare, inequality and mobility
Knowledge, organisation and politics
Culture, lifestyle and everyday life

Learning outcome

Knowledge

  • Successful participation in the class will prepare students to account for the need to apply novel methodologies to large-scale text data
  • Additionally, students will be able to identify methods of quantitative text analysis suited to answer sociological question of large-scale text data

 

Skills

  • Students will gain facility with working with R in particular as it relates to quantitative text analysis
  • Students will be able to evaluate and put into perspective the benefits and complementarities of quantitative text analysis with traditional forms of text analysis

 

Competencies

  • Students will be able to plan sociological studies that leverage the potential of modern large-scale text data

Students will be able to 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; having attended an Introduction to R class is strongly advised.

Continuous feedback during the course of the semester
Feedback by final exam (In addition to the grade)
Peer feedback (Students give each other feedback)
ECTS
7,5 ECTS
Type of assessment
Portfolio
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.
Marking scale
7-point grading scale
Censorship form
No external censorship
Criteria for exam assessment

See learning outcome

  • Category
  • Hours
  • Lectures
  • 28
  • Preparation
  • 63
  • Exam Preparation
  • 40
  • Exam
  • 75
  • English
  • 206