Keyness analysis

Abstract

Keyness analysis is perhaps the most widely used technique within corpus approaches to (critical) discourse studies. This chapter will first define the nature of keyness, and outline the research foci that keyness analysis can be usefully employed to address (e.g. not only differences, but also similarities). It will then provide a brief historical overview of the use of frequency comparisons in corpus linguistics, from manual comparisons of the frequency of particular linguistic items to the automated comparison of the frequency of all words in two corpora (or sub-corpora). The above will also act as the springboard for the discussion of the core aspects of keyness analysis, and associated common misconceptions. 1. The unit of analysis. Most studies select the word as the unit of analysis (as indicated by the common use of the term ‘keyword’). However, this need not be the default selection: frequency comparisons can focus on any lexical or morphosyntactic unit, or particular semantic or pragmatic meanings – as long as the appropriate corpus annotation has been carried out. 2. The selection of the corpora to be compared. The discussion will focus on issues relating to the nature of ‘study’ and ‘reference’ corpus, and their relative size. 3. The issue of appropriate metrics. The discussion will focus on the difference between metrics of effect size and statistical significance, and the usefulness of combining the two types. It will also provide a comparison of the effect size metrics currently adopted by widely used corpus tools. 4. The selection of the key items that a study can usefully focus on. As an automated keyness analysis usually returns a much larger number of key items than is feasible to tackle, the approach to selection is of paramount importance, as it will determine the results and conclusions. These issues, and the associated methodological decisions and their implications, will be discussed with reference to a case study.