Module POLM150 for 2019/0
- Overview
- Aims and Learning Outcomes
- Module Content
- Indicative Reading List
- Assessment
Postgraduate Module Descriptor
POLM150: Text as Data
This module descriptor refers to the 2019/0 academic year.
Module Aims
There are three primary aims of the module. First, the module will provide an applied introduction to the use of text analysis in social scientific research. You are introduced to the entire research “pipeline” for a typical text-based project, including: a) collecting textual information online (e.g., web scraping), b) key approaches to text preprocessing and “feature extraction,” and c) supervised and unsupervised approaches to text classification. These methods are essential for data scientists interested social science questions. Second, the module introduces you to the Python programming language. Python is a popular language for scientific computing and knowledge of Python will place you at a competitive advantage in industry, government, or when pursing further education. Third, the module assessments aim to further reinforce the importance research design and thus provide students with yet another opportunity to hone critical research skills.
On successfully completing the programme you will be able to: | |
---|---|
Module-Specific Skills | 1. apply appropriate tools for collecting and preprocessing textual information; 2. understand and apply a variety of text analysis methods to answer questions in social science and public policy; 3. critically evaluate the strengths and weaknesses of particular text analytic tools for answering research questions in the social and policy sciences; |
Discipline-Specific Skills | 4. employ text analytic methods to empirically evaluate theories and hypotheses in the social and policy sciences; 5. evaluate the role of text analysis for supporting policy analysis and evaluation; 6. construct arguments based on textual data for both written and oral presentation; 7. demonstrate a strong command of research design through written and oral assessments; |
Personal and Key Skills | 8. gain a solid foundation in the Python programming language; 9. communicate effectively in speech and writing; 10. work independently and within a limited time frame to complete a specified task. |
Module Content
Syllabus Plan
Although the module’s precise content may vary from year to year, it is envisaged that the syllabus will cover the following topics:
- Programming in Python
- Collecting textual information online
- Preprocessing text for analysis and “feature selection”
- Dictionary-based methods for text classification
- Supervised and unsupervised learning for text classification
- Ideological scaling
- Using text-based measures in regression models
Learning and Teaching
This table provides an overview of how your hours of study for this module are allocated:
Scheduled Learning and Teaching Activities | Guided independent study | Placement / study abroad |
---|---|---|
22 | 128 |
...and this table provides a more detailed breakdown of the hours allocated to various study activities:
Category | Hours of study time | Description |
---|---|---|
Scheduled Learning and Teaching Activity | 22 | 11 x 2 hour lectures |
Guided Independent Study | 40 | Activities to familiarize you with the Python programming language |
Guided Independent Study | 30 | Reading and preparing for lectures |
Guided independent study | 58 | Research and analysis for final essay and presentation |
Online Resources
This module has online resources available via ELE (the Exeter Learning Environment).
- Learn Python interactively online using Code School’s free Python course: https://www.codecademy.com/learn/python
Other Learning Resources
- For more information on downloading and installing Python: https://wiki.python.org/moin/BeginnersGuide/Download
Indicative Reading List
This reading list is indicative - i.e. it provides an idea of texts that may be useful to you on this module, but it is not considered to be a confirmed or compulsory reading list for this module.
Basic reading:
- Swaroop C H, A Byte of Python. https://python.swaroopch.com.
- Diipanjan Sarkar, Text Analytics with Python: A Practical Real-World Approach (New York, NY: Springer).
- Justin Grimmer and Brandon M. Stewart (2013) “Text as Data: The Promise and Pitfalls of Automatic Content Methods for Political Texts,” Political Analysis 21 (3): 267-297.
- Michael Alvarez (eds), Computational Social Science (Cambridge, UK: Cambridge University Press).