Module overview
Aims and Objectives
Learning Outcomes
Knowledge and Understanding
Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:
- Quantitative and statistical measures in large-scale text analysis
- Methods for designing, developing, and analysing large-scale text data
- Methodological and theoretical underpinnings of different approaches to text analysis
Subject Specific Intellectual and Research Skills
Having successfully completed this module you will be able to:
- justify methodological choices in text data collection, curation and analysis
- apply text analysis methods and techniques to primary data analysis
- critically evaluate the uses, advantages, and disadvantages of using text analysis methods
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
Subject Specific Practical Skills
Having successfully completed this module you will be able to:
- perform practical text analysis techniques that are informed by environmental and social justice principles within professional, legal, and ethical frameworks?
- apply the of use general and specialist software for large-scale text analysis, management, and visualisation
Syllabus
Learning and Teaching
Teaching and learning methods
| Type | Hours |
|---|---|
| Seminar | 36 |
| Independent Study | 114 |
| Total study time | 150 |
Resources & Reading list
Textbooks
Rockwell, G. and Sinclair, S. (2016). Hermeneutica: Computer-Assisted Interpretation in the Humanities. Cambridge, MA: MIT Press.
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.
D'Ignazio, C. and Klein, L. (2020). Data Feminism. Cambridge, MA: MIT Press.
Grolemund, G. (2014). Hands-On Programming with R. Sebastopol, CA: O'Reilly Media, Inc.
Silge, J. and Robinson, D. (2017). Text Mining with R. Sebastopol, CA: O'Reilly Media, Inc..
Zong, C., Xia, R., and Zhang, J. (2021). Text Data Mining. Singapore: Springer.
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 |
|---|---|
| Project plan | 20% |
| Final project | 80% |