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Monday 1 – Friday 5 August 2022
2 hours of live teaching per day
15:00 – 17:00 CEST
This course provides a highly interactive online teaching and learning environment, using state-of-the-art online pedagogical tools. It is designed for a demanding audience (researchers, professional analysts, advanced students) and capped at a maximum of 16 participants so that the teaching team can cater to the specific needs of each individual.
By the end of this course, you will understand, and be able to apply, various text-as-data methods for answering research questions in social science.
In addition to the theoretical material, you will gain proficiency in data analytic skills using the open-source statistical programming language R.
This course is suitable for students in various social science disciplines. It is set at an advanced level, for people who already possess basic quantitative skills.
By the end of this course, you will understand the current quantitative text analysis research landscape. You will have the skills to convert texts into quantitative matrices of features, and to analyse text data using statistical methods. You will also be able to apply these methods to a text corpus in support of a substantive research question, and to critically evaluate research that uses text analysis methods.
The course is suitable for researchers, professional analysts, and advanced students.
3 credits Engage fully with class activities
4 credits Complete a post-class assignment
Chendi is an assistant professor in political science at the Department of Political Science and Public Administration, VU Amsterdam. He holds a PhD in political science from the EUI.
Chendi's research interests lie in political behaviour, political economy, comparative politics, and quantitative and computational methods.
He has published in the British Journal of Political Science, Western European Politics, Comparative European Politics, and in volumes published by Cambridge University Press.
To get the most out of this course, complete the required in-depth readings for each day, and skim at least one of the recommended readings, if listed.
Five pre-recorded lectures, introducing the course's major topics and concepts, supplement the readings.
Monday
Introduction and overview
Tuesday
Data collection, webscraping and APIs
Wednesday
Descriptive analyses and dictionary methods
Thursday
Supervised methods and unsupervised methods
Friday
Word2Vec and embeddings
The course combines pre-class assignments readings and pre-recorded videos with daily two-hour live Zoom sessions. These sessions focus on two tasks:
We want you to master the technical side of text analysis. To this end, we will distribute the R script prior to the live session so you can explore the code at your own pace, but we will go through the code and models together during the sessions.
We will also get to know each other, and each other's projects, and explore how we can use text data to answer your research questions. There will be problem sets after each live session. We will discuss these assignments, and any problems you may have, together the following day.
You can share thoughts and ask questions on our Slack channel, and the Instructor will host live Q&A sessions and social breaks. You will be able to sign up for a quick one-to-one consultation during designated office hours.
You should have basic knowledge of statistical analysis. If you have completed an introductory course in applied regression, this will suffice.
The course mainly uses R. Familiarity with it is preferred, but not necessary because a lab session early on in this course will refresh your knowledge.
If you are completely new to R, take Akos Mate's course Introduction to R before signing up to this one.
Each course includes pre-course assignments, including readings and pre-recorded videos, as well as daily live lectures totalling at least three hours. The instructor will conduct live Q&A sessions and offer designated office hours for one-to-one consultations.
Please check your course format before registering.
Live classes will be held daily for three hours on a video meeting platform, allowing you to interact with both the instructor and other participants in real-time. To avoid online fatigue, the course employs a pedagogy that includes small-group work, short and focused tasks, as well as troubleshooting exercises that utilise a variety of online applications to facilitate collaboration and engagement with the course content.
In-person courses will consist of daily three-hour classroom sessions, featuring a range of interactive in-class activities including short lectures, peer feedback, group exercises, and presentations.
This course description may be subject to subsequent adaptations (e.g. taking into account new developments in the field, participant demands, group size, etc.). Registered participants will be informed at the time of change.
By registering for this course, you confirm that you possess the knowledge required to follow it. The instructor will not teach these prerequisite items. If in doubt, please contact us before registering.