Module overview
Aims and Objectives
Learning Outcomes
Subject Specific Practical Skills
Having successfully completed this module you will be able to:
- apply the of use general and specialist software for large-scale text analysis, management, and visualisation
- perform practical text analysis techniques that are informed by environmental and social justice principles within professional, legal, and ethical frameworks
Subject Specific Intellectual and Research Skills
Having successfully completed this module you will be able to:
- apply text analysis methods and techniques to primary data analysis
- critically evaluate the uses, advantages, and disadvantages of using text analysis methods
- justify methodological choices in text data collection, curation and analysis
Transferable and Generic Skills
Having successfully completed this module you will be able to:
- critically evaluate and justify choices made throughout the research process
- exercise self-direction and originality in planning and delivering a data-driven research project
- effectively apply a range of communication techniques to engage a diverse and interdisciplinary audience
Knowledge and Understanding
Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:
- Methods for designing, developing, and analysing large-scale text data
- Methodological and theoretical underpinnings of different approaches to text analysis
- Quantitative and statistical measures in large-scale text analysis
Syllabus
Learning and Teaching
Teaching and learning methods
| Type | Hours |
|---|---|
| Seminar | 36 |
| Independent Study | 114 |
| Total study time | 150 |
Resources & Reading list
Textbooks
Karsdorp, F., Kestemont, M., and Riddell, A. (2021). Humanities Data Analysis: Case Studies with Python. Princeton: Princeton University Press.
Grimmer, J, Roberts, M. E., and Stewart, B. M. (2022). Text as Data: A New Framework for Machine Learning and the Social Sciences. Princeton and Oxford: Princeton University Press.
Silge, J. and Robinson, D. (2017). Text Mining with R. Sebastopol, CA: O'Reilly Media, Inc..
D'Ignazio, C. and Klein, L. (2020). Data Feminism. Cambridge, MA: MIT Press.
Zong, C., Xia, R., and Zhang, J. (2021). Text Data Mining. Singapore: Springer.
Rockwell, G. and Sinclair, S. (2016). Hermeneutica: Computer-Assisted Interpretation in the Humanities. Cambridge, MA: MIT Press.
Grolemund, G. (2014). Hands-On Programming with R. Sebastopol, CA: O'Reilly Media, Inc.
Assessment
Summative
This is how we’ll formally assess what you have learned in this module.
| Method | Percentage contribution |
|---|---|
| Project plan | 20% |
| Final project | 80% |
Referral
This is how we’ll assess you if you don’t meet the criteria to pass this module.
| Method | Percentage contribution |
|---|---|
| Final project | 100% |
Repeat
An internal repeat is where you take all of your modules again, including any you passed. An external repeat is where you only re-take the modules you failed.
| Method | Percentage contribution |
|---|---|
| Final project | 80% |
| Project plan | 20% |