Quantitative Text Analysis and Machine Learning using R

About

The abundance of textual data in modern times provides a rich source of information on social and political behaviour. 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.

Course leader

Zoe Greene is a Reader at the University of Strathclyde. Her research interests include quantitative text analysis and machine learning approaches to studying political parties, parliaments and elections. Her 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 zoegreene.com.

Target group

Social science researchers

Course aim

The goal of this course is to equip you with the knowledge and skills to harness computational approaches to analyse text data. By the end of the course, you will:

- understand the conceptual framework and data management techniques necessary for analysing text data as data, including the fundamentals of natural language processing using machine learning;
- be familiar with common computational approaches to text analysis that are widely used in political and social sciences; and
- be introduced to advanced methods for managing and analysing text data using embeddings approaches.

Overall, the course aims to provide you with the tools and techniques you need to develop projects that utilise computational approaches to text analysis.

Fee info