EH295-7-SL-CO:
Quantitative Text Analysis
2022/23
Essex Summer School in Social Science Data Analysis
Colchester Campus
Summer & Long Vacation
Postgraduate: Level 7
Current
Monday 24 April 2023
Wednesday 04 October 2023
15
03 February 2023
Requisites for this module
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Our world is increasingly being recorded as digital text, capturing human knowledge and interactions to an unprecedented level and providing a rich source of data for researchers across different academic disciplines and subjects. Consequently, computational text analysis methods and tools are becoming increasingly popular and starting to make their way into the core research methods curriculum.
This course is designed to provide social science researchers an entry point to computational text analysis. Participants will gain hands-on experience designing and implementing a quantitative text analysis research project and will learn to discuss, evaluate and interpret the results. Each class consists of a 2-hours lecture followed by a 1.5 hours lab in which participants apply the methods covered in the lecture in R.
We will start with an overview of computational text analysis methods and discuss examples of their application across multiple disciplines and research fields. We will then survey the main ways in which text data can be acquired and present several major online text data sources.
The first steps in a text analysis research project – covering imputing, importing, manipulating and storing text data under different formats, as well as cleaning and processing it – often prove to be the most challenging for beginners. After addressing this initial set of issues we will study: the main ways in which text data can be turned into numbers; descriptive methods such as frequency tables and word clouds; automated dictionary methods (such as those developed to extract different emotions from text); text comparison methods (which are often used to study the diffusion and evolution of laws, policies and ideas); and text scaling methods (such as those used by political scientists to map the positions of political actors in the ideological space).
Finally, the course provides an introduction to machine learning applied to text data: supervised classification (routinely used in multiple disciplines to label large volumes of text documents based on a small subset of coded data) and unsupervised learning methods (as a very light introduction to topic modelling).
No information available.
At the end of the course, participants will have an understanding of the current quantitative text analysis research landscape, the ways in which computational text analysis can be applied to their area of interest and the main data sources, tools and methods available for further exploration.
Participants will also gain hands-on experience designing and implementing a quantitative text analysis research project in R and will be able to discuss and interpret the results and acknowledge the limitations of the methods used.
Course prerequisites
Students are expected to have working knowledge of R, and be familiar with basic (undergraduate level) research design and statistical analysis notions.
Module information will be made available at https://essexsummerschool.com/.
Please contact essexsummerschoolssda@essex.ac.uk and govpgquery@essex.ac.uk with any queries.
No information available.
This module does not appear to have a published bibliography for this year.
Assessment items, weightings and deadlines
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Exam format definitions
- Remote, open book: Your exam will take place remotely via an online learning platform. You may refer to any physical or electronic materials during the exam.
- In-person, open book: Your exam will take place on campus under invigilation. You may refer to any physical materials such as paper study notes or a textbook during the exam. Electronic devices may not be used in the exam.
- In-person, open book (restricted): The exam will take place on campus under invigilation. You may refer only to specific physical materials such as a named textbook during the exam. Permitted materials will be specified by your department. Electronic devices may not be used in the exam.
- In-person, closed book: The exam will take place on campus under invigilation. You may not refer to any physical materials or electronic devices during the exam. There may be times when a paper dictionary,
for example, may be permitted in an otherwise closed book exam. Any exceptions will be specified by your department.
Your department will provide further guidance before your exams.
Overall assessment
Reassessment
Module supervisor and teaching staff
No
No
No
Available via Moodle
No lecture recording information available for this module.
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