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.

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.

The course covers the following topics:

• The fundamentals of quantitative text analysis using machine learning.
• Estimation of dictionary, sentiment, topic, and scaling models.
• Methods for text classification using supervised machine learning.
• Word embeddings and textual representation models.

Also available as L2498.

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.

Christine Sylvester is a Chancellor's Fellow (Assistant Professor) in the Department of Government and Public Policy at the University of Strathclyde. She specializes in quantitative text analysis and machine learning, with research interests in comparative politics, intra-party agenda setting, policy formation, and party leader survival. Christine is one of the creators of the ParlEE dataset of parliamentary speeches and previously worked on the EU-in-Action project, analysing how EU institutions respond to public demands

Discounted fee of £450 available to academic, non-profit and government organisations only. Enter discount code MC40-F143EA33EDC9 at checkout. 

Fee is £750 for all other participants.

**PLEASE NOTE THAT THE START DATE FOR THIS COURSE IS TO BE ANNOUNCED (AUTUMN 2025).**