Sociology

Ideology

Content analysis

Qualitative research

Qualitative analysis

Social science

Latent Dirichlet allocation

Collocation (remote sensing)

Discourse analysis

Operationalization

Politics

Epistemology

Machine learning

Philosophy

Political science

Artificial intelligence

Exploit

Topic model

Chemistry

Data science

Critical discourse analysis

Computer science

Linguistics

Genre analysis

Computer security

Chromatography

Quantitative analysis (chemistry)

International Journal of Social Research Methodology

Quantitative text analysis tools have become increasingly popular methods for the operationalization of various types of discourse analysis. However, their application usually remains fairly simple and superficial, and fails to exploit the resources which the digital era holds for discourse analysis to their full extent. This paper discusses the discourse-analytic potential of a more complex and advanced text analysis tool, which is already frequently employed in other approaches to textual analysis, notably topic modelling. We argue that topic modelling promises advances in areas where discourse analysis has traditionally struggled, such as scaling, repetition, and systematization, which go beyond the contributions of simpler frequency and collocation counts. At the same time, it does not violate the epistemological premises and methodological ethos of even the more radical theories of discourse, we will demonstrate. Finally, we present two small case studies to show how topic modelling – when used with appropriate parameters – can straightforwardly enhance our ability to systematically investigate and interpret discourses in large collections of text. Abbreviations: CDA: Critical Discourse Analysis; LDA: Latent Dirichlet Allocation

Topic models meet discourse analysis: a quantitative tool for a qualitative approach