College of Social Sciences and International Studies
Text as Data
Module POLM150 for 2017/8
Module POLM150 for 2017/8
- Overview
- Aims and Learning Outcomes
- Module Content
- Indicative Reading List
- Assessment
Postgraduate Module Descriptor
POLM150: Text as Data
This module descriptor refers to the 2017/8 academic year.
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).