​​RSS02.E5 Introduction to Text as Data​ - Confirmed


Facing the massive volumes of text data that are available in digital format and
valuing their potential, over recent years social scientists have increasingly turned
to methods that rely on the support of computer power, so- called automated text analysis methods. 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 provides an introduction to 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, Data collection, Data selection, Feature Selection, Text Representation, Rule-based classification  Supervised classification, Unsupervised approaches, Intro advanced methods (e.g., Scaling methods, Embeddings, Multilingual text analysis). 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 Monday, Tuesday, Wednesday, and Thursday. 


26 June 2023 - 30 June 2023
Course Fee

Regular: €995
Students & PhD's: €645

Early Bird Regular: €895 (application deadline* April 1st) 
Early Bird Students & PhD's: €580,50 (application deadline* April 1st)

Scholarships and discounts Find more information here
Application deadline

May 15th

*Your application is only completed when the course fee has been paid

Course leader Fabienne Lind
Level of participant
  • Master
  • PhD
  • PostDoc
  • Professional
Admission requirements 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.
Admission documents
  • ​To get the student/PhD discount you need to upload a copy of your Student card or other proof of registration
  • If you are not a student/PhD, you can upload an empty document under 'Student Card'.
Mode of Study On Campus
ECTS 2 or 4 Find more information here
Location Radboud University