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GESIS Training Courses
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Wiss. Koordination

Verena Kunz

Administrative Koordination

Claudia O'Donovan-Bellante

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Introduction to Computational Text Analysis with R

About
Veranstaltungsort:
Online via Zoom
Course times:
09:00-12:00 and 13:00-16:00 (CET / including a 15-minute break per session)
 
General Topics:
Course Level:
Beginner
Format:
Workshop
Software used:
R
Duration:
3 days
Language:
English
Fees:
Students: 330 €
Academics: 495 €
Commercial: 990 €
 
Keywords
Additional links
Dozierende: Lea Kaftan, Jens Wäckerle

Referenteninformationen - Lea Kaftan

Referenteninformationen - Jens Wäckerle

Seminarinhalt

Registration deadline: 12.04.2027  
Registration after this date might be possible, but is subject to sufficient demand and availability of places.
 
Computational text analysis is a fast-growing and widely used methods field, allowing researchers to structure and study large corpora of texts in-depth from a multitude of theoretical backgrounds. The workshop introduces key concepts, standard methods, and the research logic underlying computational text analysis. It is designed for researchers with little or no experience in computational text analysis who want to learn the fundamentals of the field and apply them to their own research.
 
Participants will learn to pre-process their text data and to apply and validate basic supervised and unsupervised methods such as dictionaries, classification methods (with and without large language models), and clustering methods (i.e., topic models) to their own text data using the programming language R. Participants will additionally learn the benefits and pitfalls of research designs based on the quantitative analysis of large text corpora, including ethical issues, potential biases, and implications of text choices. After the course, participants will be able to choose appropriate designs for their own research questions and apply standard methods of text analysis in R.
 
Organizational structure of the course
The workshop contains both lectures and practical exercises. The lectures aim to provide a general understanding of the research logic and methods. The practical exercises deepen participants' understanding of the methods and teach them how to apply them to their own data. During the exercises, the lecturers will be available for support, troubleshooting, and questions. Participants can bring their own data or work with data provided by the lecturers.


Zielgruppe

You will find the course useful if:
  • you want to work with text data in your research for the first time.
  • you want to refresh prior basic knowledge in text analysis.
  • you want to learn how to pre-process text data for quantitative analyses.
  • you want an overview of standard computational approaches to analyzing text data.


Lernziel

By the end of the course, you will:
  • be able to design and execute your own research based on large text corpora.
  • be able to pre-process, analyze, and visualize text data using R.
  • understand the principles of supervised and unsupervised methods for computational text analysis.
  • be able to independently familiarize yourself with more advanced methods.


Voraussetzungen

  • Basic knowledge of R is a prerequisite, as the lecturers cannot provide a general introduction to R during the course. Participants should know how to import data (such as csv files), install packages, and work with objects in R.
If you are looking for an introduction or refresher to R, we recommend the GESIS Workshop "Introduction to R" (11.01.-19.01.2027).
 
Software requirements
R (at least version 4.4.1) and RStudio. Prior to the workshop, participants will receive an R script to install all required packages.


Zeitplan

Literaturempfehlungen