400007 SE Advanced quantitative text analyses (2022W)

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 computer-assisted or 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 is aimed at people with some knowledge of automated text analysis who want to use this method in their PhD and/or want to deepen their expertise of the matter.

The course covers topics related to data collection, data processing, quality control, and the critical interpretation of results.

We cover the following topics:
- What kind of questions can be answered with automated text analysis
- Recap R, Introduction R Markdown and GitHub
- Scraping and Using APIs
- Pre-processing
- Regular Expressions and Classification with dictionaries
- Machine Learning and Classification
- Topic modeling/k-means
- Multilingual text analysis
- Validation
- Ethics and Data Security
- Critical reflection on the methods

All topics are introduced with a lecture type approach and then illustrated with practical examples. The practical part consists of guided coding sessions, where we work together through prepared code, and of small coding challenges, which are worked on alone or in groups.

We will work mainly with R. Please bring your own laptop.

Please note: The prerequisite for participation in advanced seminars is the conclusion of the doctoral thesis agreement.