Topic modelling in corpus-based discourse analysis: Uses and critiques

This discussion paper aims to take stock of the main uses and critiques of topic modelling in studies that combine corpus linguistics and discourse analysis. Topic modelling is a cover term for a collection of semiautomated techniques that aim to analyse the content of texts. While the paper does not provide a complete synthesis of the use of topic modelling in corpus linguistics, it aims to give an overview of recent uses and critiques of topic modelling within corpus-based discourse analysis and related fields. Such recent uses include topic modelling for corpus exploration and combining topic modelling with other analytic techniques such as keywords analysis, showing an increasing engagement with topic modelling in corpus linguistic studies of discourse(s). However, the critiques levelled at topic modelling are not inconsiderable, although some researchers do see it as a strong and effective method. One conclusion from the research reviewed in this article is that topic modelling can be enhanced through the use of methods from both corpus linguistics and discourse analysis which can reduce some of its limitations. In this respect, it can be argued that linguists have a lot to offer to non-linguistic fields where topic modelling is widely used and that interdisciplinary collaborations could be a fruitful endeavour. The paper is accompanied by invited expert commentaries written by linguists with relevant experience in applying topic modelling. These commentaries provide further comment on its uses and critiques in relation to corpus-based discourse analysis, confirming, challenging, elaborating or extending the points made here. While this article primarily focuses on corpus-based discourse analysis, many of the points should also be of interest to scholars in other fields. Taken together, it is clear that continued exploration of and debate on topic modelling is a worthwhile endeavour, and that new technological developments necessitate such critical engagement.