Human communication is increasingly recorded as digital text, which constitutes big data that can be used to study numerous scientific and real-world problems. The goals of this course are to (i) provide an introduction to quantitative methods designed to analyze text, (ii) give an overview over common applications of these methods in economics and the social sciences, and (iii) illustrate the potential of text-as-data methods to ask new research questions and find new answers to existing problems.

The course provides an overview over the most common text-as-data methods as well as their typical areas of application:

- Prerequisites (text import, creation of corpora, pre-processing, creation of document-term matrices, lemmatization)
- Text statistics (e.g., frequency analysis, measures of readability, similarity indices)
- Generic and customized dictionaries
- Sentiment analysis
- Text classification using reference texts and supervised learning
- Topic modeling

Teaching in campus meetings takes place in interactive lectures requiring active class participation, with assigned homework between the occasions. All teaching is in English.

The campus meetings are planned as follows:

  • Session 1: Wednesday, April 19, 10:00 – 16:00
  • Session 2: Thursday, April 20, 10:00 – 16:00
  • Session 3: Wednesday, May 3, 10:00 – 16:00
  • Session 4: Thursday, May 4, 10:00 – 16:00
  • Session 5: Thursday, May 11, 10:00 – 16:00
  • Session 6: Thursday, June 8, 10:00 – 16:00

Rooms for the different sessions will be announced in due time. Lunch break from 12:00 – 13:00.

Before the start of the course, please register for a presentation (see the enclosed course scedule for detailed information).

The course has a maximum of 15 places. The seats are accessible via the "first come, first served" principle. The last day to apply is February 7, 2023.

Application form Word, 58.8 kB.

Course description and schedule Pdf, 696.1 kB.

Course syllabus Pdf, 128.4 kB.

If you have any questions, please contact the course coordinator:

Associate Professor, Docent Marcel Garz, JIBS