D01-03AM: Introduction to Quantitative Text Analysis

This course covers basic methods of handling large volumes of text data. A mix of interactive lectures and guided coding sessions will enable you to implement your first text as data research designs.

Facing the massive volumes of text data that are available in digital format and valuing their potential, methods that rely on the support of computer power, so- called automated text analysis methods, have become part of the standard methods repertoire of social scientists. The text-as-data methods are used to draw reproducible and valid inferences or meanings from documents. As an enhancement of the more classical manual methods of content analysis, automated methods of text analysis are becoming prevalent in disciplines that are overall increasingly computationally oriented. This course introduces various text as data methods. It includes aspects related to data collection, data processing, quality control, and the critical interpretation of results. In detail, the following topics are covered:  Motivations and applications for using text as data methods, Text Representation, Feature Selection, Data collection, Manual Coding, Dictionaries, Supervised Machine Learning and Topic modeling. 

All topics are introduced with a lecture type approach and then illustrated with practical examples. The lecture part consists of input by the instructor (i.e., covering the basics of each topic, highlighting latest methods research, and introducing resources) and shorter interactive parts (i.e., reflection and discussions on different methods in plenary and small groups). The practical part consists of guided coding sessions, where we work together through prepared code. In addition, small coding challenges (1h), which are worked on alone or in groups after the class hours will be assigned Tuesday and Thursday. We will work mainly with the programming language R and the development environment RStudio. Basic practical knowledge of R and RStudio is therefore a prerequisite for participation in the course.

After this course, participants will be able 1) to make an informed decision about a suitable method for a given application scenario, 2) to practically apply basic text as data methods, and 3) to critically evaluate results.

This course is open to all researchers and practitioners aiming at bringing their research to the next level. It is particularly designed for people with no or little prior knowledge of automated text analysis but who want to use this method in their research projects and/or want to deepen their expertise of the matter.

Readings: 

Van Atteveldt, W., Trilling, D., & Calderon, C. A. (2022). Computational analysis of communication. John Wiley & Sons. http://cssbook.net

This description is subject to change at the discretion of the Instructor

2 Credits

For completion of all work before and during the course, as outlined by the Instructor, and 90% participation and attendance of the course.

2 Additional Credits

Course specific extra assignments. These can include submitting assignments before the course, daily assignments, and/or a final assignment to be completed after the course as decided by the Instructor.  This description is subject to change at the discretion of the Instructor.