Postgraduate Course: Text Mining for Social Research (EFIE11486)
Course Outline
| School | Edinburgh Futures Institute |
College | College of Arts, Humanities and Social Sciences |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Available to all students |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | There is increasing demand for researchers and policy-makers to have basic familiarity with the skills required to conduct text mining and analysis. This is a hands on programming course that requires no previous experience or skills, taking novices from their different disciplines to a point where they can do useful analytical processes with textual data. As part of the Future Governance pathway, this is a data-led course using contemporary or archived materials, such as political speeches, public records or media sources. |
| Course description |
During this course you will learn from scratch the theory and practice of analysing text documents with code. The course is suitable for participants who have no prior experience of text analysis or programming in Python. At the end of this course students will know the basics of the theory behind text mining and will have skills to prepare, search, analyse and create visualisations from text documents at scale. The contexts and examples we will use will be relevant to social research.
The practical parts of this course are taught in the programming language Python. Initially, the core basic Python skills are introduced and the students are taught how to set up their virtual programming environment. Participants will then learn how to read in textual files and carry out the initial processing required for text manipulation. The course will also cover concordances, frequency distributions, lexical dispersions, collocations, part-of-speech tagging, named entity recognition as well as network creation and draw on sample datasets relevant to social and political research. This then allows us to introduce the more complex analysis and visualisation techniques required to extract information from large text datasets.
Through worked examples, pair programming exercises and a final project, learners will produce original pieces of work involving the practical skills they acquired.
Edinburgh Futures Institute (EFI) - Hybrid Course Delivery Information:
The Edinburgh Futures Institute delivers many of its courses in hybrid mode. This means that you may have some online students joining sessions for this course. To enable this, the course will use technologies to record and live-stream student and staff participation during their teaching and learning activities.
Students should be aware that:
- Classrooms used in this course will have additional technology in place: in some cases, students might not be able to sit in areas away from microphones or outside the field of view of all cameras.
- All presentations, and whole class discussions will be recorded (see the Lecture Recording and Virtual Classroom policies for more details).
You will need access to a personal computing device for this course. Most activities will take place in a web browser, unless otherwise stated. We recommend using a device with a screen, a physical keyboard, and internet access.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
|
Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Information for Visiting Students
| Pre-requisites | None |
| High Demand Course? |
Yes |
Course Delivery Information
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| Academic year 2026/27, Available to all students (SV1)
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Quota: 30 |
| Course Start |
Semester 2 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Lecture Hours 5,
Seminar/Tutorial Hours 2,
Supervised Practical/Workshop/Studio Hours 13,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
176 )
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
The course will be assessed by means of the following components:
1) Individual Coursework (Text Analysis Project) (100%)
Project and report (blog posts, including a data analysis with visualisations, a testable hypothesis and results) of 1200 words (2 blog posts of max. 600 each) plus the accompanying coding notebook.
This includes a paragraph that reflects on the peer learning during the course.
Learning Outcomes Assessed by Component: 1, 2, 3, 4, 5 |
| Feedback |
Feedback on any formative assessment may be provided in various formats, for example, to include written, oral, video, face-to-face, whole class, or individual. The Course Organiser will decide which format is most appropriate in relation to the nature of the assessment.
Feedback on both formative and summative in-course assessed work will be provided in time to be of use in subsequent assessments within the course.
Feedback on the summative assessment(s) will be provided in written form via Learn, the University of Edinburgh's Virtual Learning Environment (VLE).
Formative Feedback Opportunity:
Formative feedback is ongoing feedback which monitors learning and is intended to improve performance in the same course, in future courses, and also beyond study.
Students will receive the following feedback:
- Solutions to in-course programming tasks will be provided.
- In person coding feedback.
- Written feedback by academic staff on the final project submission. |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Demonstrate a critical understanding of the main areas of study linked to the use of technology in text and data mining.
- Explain and use key technologies and formats used in data analysis.
- Develop original and creative responses to data driven problems.
- Demonstrate their ability to deliver - in verbal and written form - coherent, balanced arguments surrounding the use of data.
- Work in a peer relationship and make an identifiable contribution to change and development and/or new thinking.
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Reading List
Essential Reading:
Ignatow, Gabe, and Rada Mihalcea. Text mining: A guidebook for the social sciences. Sage Publications, 2016. https://dx.doi.org/10.4135/9781483399782.n1
Chapter 1: Social Science and the Digital Text Revolution (essential reading)
Chapter 2: Research Design Strategies, Levels of Analysis, p. 18 (recommended reading)
Chapter 5: Basic Text Processing (recommended reading)
Chapter 7: Text Analysis Methods from the Humanities and Social Sciences, Visualisations Tools, pp. 83-86 (recommended reading)
Chapter 12: Information Extraction, Entity Extraction, p. 130 (recommended reading)
Holy, Dirk. Text analysis in Python for social scientists : prediction and classification. Cambridge University Press, 2020. https://www-cambridge-org.ezproxy.is.ed.ac.uk/core/elements/text-analysis-in-python-for-social-scientists/BFAB0A3604C7E29F6198EA2F7941DFF3 (you need to be logged into your university account or the university VPN to access this link online)
Chapter 1: Prerequisites
Chapter 2: What's in a Word
Chapter 3: Regular Expressions
How to write an engaging blog, The University of Edinburgh, https://information-services.ed.ac.uk/learning-technology/learning-and-teaching-technologies/academic-blogging-service/introduction-to-5
Further Reading:
Lacey, Nichola. Python by Example: Learning to Program in 150 Challenges. Cambridge University Press, 2019. https://doi-org.ezproxy.is.ed.ac.uk/10.1017/9781108591942
Bird, Steven, Ewan Klein, and Edward Loper. Natural language processing with Python: analyzing text with the natural language toolkit. " O'Reilly Media, Inc.", 2009.
SpaCy API documentation, https://spacy.io/api |
Additional Information
| Graduate Attributes and Skills |
Not entered |
| Keywords | Text and Data Mining,Natural Language Processing,Social Research,Programming |
Contacts
| Course organiser | Mrs Clare Llewellyn
Tel:
Email: cllewell@exseed.ed.ac.uk |
Course secretary | Mr Matt Bryant
Tel:
Email: Matt.Bryant@ed.ac.uk |
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