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DEGREE REGULATIONS & PROGRAMMES OF STUDY 2022/2023

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DRPS : Course Catalogue : School of Social and Political Science : Postgrad (School of Social and Political Studies)

Postgraduate Course: Computational Text Analysis (PGSP11584)

Course Outline
SchoolSchool of Social and Political Science CollegeCollege of Arts, Humanities and Social Sciences
Credit level (Normal year taken)SCQF Level 11 (Postgraduate) AvailabilityAvailable to all students
SCQF Credits20 ECTS Credits10
SummaryThis course will give students training in the use of computational text analysis techniques. The course will prepare students for dissertation work that uses textual data and will provide hands-on training in the use of the R and Python programming languages. The course will provide a venue for seminar discussion of examples using these methods in the empirical social sciences as well as lectures on the technical and/or statistical dimensions of their application.

There are no formal pre-requisites for enrolment in this course. However, for those with no background in mathematics or statistics it is preferable that they take Core quantitative data analysis 1 and 2 (SCIL11009).
Course description The course has two principal learning aims: 1) training in the application of computational text analysis techniques with the use of R or Python programming languages; 2) an in-depth introduction to both the background of text analysis techniques and more recent examples of computational text analysis. Computational text analysis has taken off as a field in recent years, as attested by three recent review articles devoted to the topic across Sociology, Political Science, and Economics (see https://www.annualreviews.org/doi/abs/10.1146/annurev-polisci-052615-025542 and https://www.annualreviews.org/doi/abs/10.1146/annurev-soc-081715-074206 and https://www.aeaweb.org/articles?id=10.1257/jel.20181020).

The course will give students the ability to conduct their own research using these techniques as well as the requisite background in the history of text analysis. Text analysis has a history that predates the use of computational methods and students will be introduced to the conceptual elements of its application historically, as well as the advances afforded by computational techniques. Each lecture will focus on the more technical and/or statistical dimensions of computational text analysis techniques. Seminars will be devoted to the discussion of applied examples. The seminars will be organized by thematic topic and readings will be selected that speak to the technical aspects of the most recent lecture. Topics that may be covered include: word frequency analysis; topic modelling; structural topic modelling; and word embedding.

Each week students will be expected to attend the lecture or seminar, depending on the week as these will alternate. In seminar weeks, students will be expected to read all of 2-3 essential readings, which will form the basis for a class discussion.

NOTE: For the first two weeks, students will complete introductory R exercises. These will be run out of the Research Training Centre, who will be offering introductory R instruction in weeks 1 and 2 of Semesters 1 and 2. These will be formative but provide the requisite basic training in the use of R and RStudio. Lectures will still run in these weeks but there will be no tutorials.

In subsequent lecture weeks, students will also be expected to complete a formative coding exercise in either the R or Python programming language. These will be set by the instructor and made accessible to students. Every week, students will also have a tutorial where they can raise questions related to the technical components of the lecture and discuss coding issues and concerns.

Assessment will take the form of one major summative assessment. This will be a 4000 word essay on a subject of the students┬┐ choosing (with prior approval by the instructor) using a dataset of their own choosing and applying at least one computational text analysis technique. The students will provide the code they used in reproducible format and will be assessed on both the substantive content of their essay contribution as well as their demonstrated competency in coding and text analysis.

Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements None
Information for Visiting Students
Pre-requisitesNone
High Demand Course? Yes
Course Delivery Information
Academic year 2022/23, Available to all students (SV1) Quota:  30
Course Start Semester 2
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 200 ( Seminar/Tutorial Hours 20, Programme Level Learning and Teaching Hours 4, Directed Learning and Independent Learning Hours 176 )
Assessment (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Additional Information (Assessment) Summative essay + code for text analysis used (100%)
Feedback Coding choices can be discussed in tutorial settings with the TA
No Exam Information
Learning Outcomes
On completion of this course, the student will be able to:
  1. Be competent in R or Python for computational text analysis
  2. Have knowledge of the applied literature in computational text analysis
  3. Provide reproducible workflows for text analyses with R Markdown or Jupyter notebooks
  4. Appraise the relationship between theory, measurement, and method in applied examples of computational text analysis techniques
  5. Design, conduct, and discuss a piece of research using computational text analysis techniques
Reading List
- Klaus Krippendorff. Content Analysis: An Introduction to Its Methodology. Third Edition. Sage. 2013.
- Daniel Jurafsky and James H. Martin Speech and Language Processing, 2nd Edition. Prentice Hall. 2008.
- Kozlowski, AC et al. "The Geometry of Culture: Analyzing the Meanings of Class through Word Embeddings." American Sociological Review. 84(5) 905┬┐949.
- Nelson, LK, 2020. "Computational Grounded Theory: A Methodological Framework." Sociological Methods & Research. 49(1):3-42.
Additional Information
Graduate Attributes and Skills o Communication and IT skills
o Autonomy and self-directed research
o Reflection and critical faculties
KeywordsNot entered
Contacts
Course organiserDr Chris Barrie
Tel:
Email: cbarrie6@ed.ac.uk
Course secretaryMr John Riddell
Tel: (0131 6)50 9975
Email: John.Riddell@ed.ac.uk
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