Quantitative Text Analysis and Machine Learning using R

About

This course offers you with an immersive online learning environment that employs state-of-the-art pedagogical tools. The abundance of textual data in modern times provides a rich source of information on social and political behavior. As a result, social scientists have increasingly turned to computational or computer-assisted methods to extract insights from this data. This course is designed to equip you with the foundational knowledge and skills required to manage and analyse textual data using R.

ECTS Credits
3 ECTS credits awarded for engaging fully in class activities.
1 additional ECTS credit awarded for completing a post-course assignment

Course leader

Zachary Greene is a Reader at the University of Strathclyde. His research interests include quantitative text analysis and machine learning approaches to studying political parties, parliaments and elections. His work has been published in journals including, the British Journal of Political Science, European Journal of Political Research, Journal of European Public Policy, Party Politics and Electoral Studies. You can find more information on Dr Greene’s research, teaching and ongoing projects at zacgreene.com

Target group

Social science researchers

Course aim

Upon completion of the course, you will:
- Develop a conceptual understanding of basic approaches to natural language processing;
- Gain familiarity with common quantitative text analysis techniques used in political science literature and be able to interpret the results; and
- Learn how to implement the most widely used methods employed by social scientists

Fee info