Quantitative Text Analysis for Social Scientists
Module Code: ECP77524
- ECTS Credit: 5
- Mandatory/Optional: Optional
- Semester/Term Taught: Hilary Term
- Module Coordinator: Professor Constantine Boussalis
Module Aims
To introduce students to a variety of methods used to systematically extract and analyze quantitative data from textual information for social science research.
Learning Outcomes
On completion of the module, students will be able to:
- Use natural language processing and machine learning methods to quantitatively study textual information. Evaluate the entire “pipeline” of a quantitative research project, ranging from the acquisition of documents from digitized or non-digitized sources, extracting and pre-processing features from a corpus, and effectively analyzing the collection of documents.
- Analyze textual data using lexicon-based, supervised, and unsupervised methods.
- Critically evaluate the strengths and weaknesses of various text analysis tools and gain an appreciation for proper application.
- Develop and hone critical research skills through a written research report.
Module Content
This module focuses on a range of computational tools—stemming from the fields of machine learning and natural language processing (NLP)—that are essential for large-scale analyses of text information. The aim is to provide students with a hands-on introduction to collecting, processing, and analyzing “text-as-data” for the purpose of answering important social science research questions. The module will also cover corpus acquisition methods as well as social media research applications. Students will apply these skills to produce a state-of-the-art research report based on a novel collection of text documents and meta-data.
Recommended Reading List
- Grimmer, J., & Stewart, B. M. (2013). Text as data: The promise and pitfalls of automatic content analysis methods for political texts. Political analysis, 21(3), 267-297.
- Salganik, M. J. (2019). Bit by bit: Social research in the digital age. Princeton University Press.
Assessment
Assessment comprises 50% continuous assessment and a 50% project.
Module Website
Blackboard