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400007 SE Advanced quantitative text analyses (2022W)
Advanced level Methods Seminar
Continuous assessment of course work
Labels
ON-SITE
Registration/Deregistration
Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).
- Registration is open from Th 01.09.2022 09:00 to Th 29.09.2022 23:59
- Deregistration possible until Mo 31.10.2022 23:59
Details
max. 15 participants
Language: English
Lecturers
Classes (iCal) - next class is marked with N
- Monday 14.11. 09:45 - 14:45 Seminarraum 11 Vernetzungsraum für Vienna Doctoral School of Social Sciences, Kolingasse 14-16, OG01
- Tuesday 15.11. 09:45 - 13:00 Seminarraum 11 Vernetzungsraum für Vienna Doctoral School of Social Sciences, Kolingasse 14-16, OG01
- Wednesday 16.11. 09:45 - 14:45 Seminarraum 11 Vernetzungsraum für Vienna Doctoral School of Social Sciences, Kolingasse 14-16, OG01
- Thursday 17.11. 09:45 - 14:45 Seminarraum 11 Vernetzungsraum für Vienna Doctoral School of Social Sciences, Kolingasse 14-16, OG01
- Friday 18.11. 09:45 - 14:45 Seminarraum 11 Vernetzungsraum für Vienna Doctoral School of Social Sciences, Kolingasse 14-16, OG01
Information
Aims, contents and method of the course
Assessment and permitted materials
Minimum requirements and assessment criteria
- Final paper: application of one or several automated text analysis methods on a topic related to the PhD thesis or a topic of free choice (80%)
- Assessment of class participation (20%)
- Assessment of class participation (20%)
Examination topics
Reading list
Association in the course directory
Last modified: Fr 23.09.2022 18:10
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 methodsAll 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.