Computational methods are transforming research practice across the disciplines. For social scientists these methods offer a number of valuable opportunities, including creating new datasets from digital sources; unearthing new insights and avenues for research from existing data sources; and improving the accuracy and efficiency of fundamental research activities.
In this three-day course you will learn how to apply computational methods for the collection, management and analysis of text data. Using Python and R, you will develop skills in collecting data from the web; preparing textual data for computational analysis; and core methods of text data analysis (e.g., sentiment analysis, topic modelling).
This course is suitable for social science researchers from any methodological background (e.g., qualitative, quantitative) and who are new to the use of computational methods in their research. Participants are not expected to have any experience of using Python or R before attending this course.
This repository houses the materials underpinning the three-day course run by Dr Diarmuid McDonnell, University of the West of Scotland. The course was first run in March 2025.
The course programme can be viewed here:
- Day 1: https://github.com/SGSSSonline/text-analysis/blob/main/sgsss-2025-css-day-1-outline-2025-03-07.pdf
- Day 2-3: https://github.com/SGSSSonline/text-analysis/blob/main/sgsss-2025-css-day-2-3-outline-2025-03-13.pdf
The training materials can be found in the following folders:
- code - Jupyter Notebooks and syntax files containing executable Python and R code.
- installation - Guidance on installing software on your own machines.
- presentations - PDFs and recordings of the course lectures.
- reading - lists of interesting and relevant reading materials.
I am grateful to the Scottish Graduate School of Social Sciences (SGSSS) for funding and organising this course.
Please do not hesitate to get in contact if you have queries, criticisms or ideas regarding these materials: Dr Diarmuid McDonnell