‘What is this corpus about?’: using topic modelling to explore a specialised corpus

TLDR

The paper demonstrates how topic modelling can be applied to an academic English corpus to identify prominent topics, track their evolution, classify paper types, and compare the approach to traditional corpus linguistics methods. It explains the underlying probabilistic model, outlines the steps for building a topic model—including key parameter choices—and shows how the resulting topics are explored and interpreted. The analysis shows that topics, defined by co‑occurring words, provide rich insights into corpus structure and confirm that topic modelling is especially useful for initial corpus exploration.

Abstract

This paper introduces topic modelling, a machine learning technique that automatically identifies ‘topics’ in a given corpus. The paper illustrates its use in the exploration of a corpus of academic English. It first offers the intuitive explanation of the underlying mechanism of topic modelling and describes the procedure for building a model, including the decisions involved in the model-building process. The paper then explores the model. A topic in topic models is characterised by a set of co-occurring words, and we will demonstrate that such topics bring us rich insights into the nature of a corpus. As exemplary tasks, this paper identifies the prominent topics in different parts of papers, investigates the chronological change of a journal, and reveals different types of papers in the journal. The paper further compares topic modelling to two more traditional techniques in corpus linguistics, semantic annotation and keywords analysis, and highlights the strengths of topic modelling. We believe that topic modelling is particularly useful in the initial exploration of a corpus.