Keyword analysis is a Corpus Linguistics technique that identifies words occurring with unusual frequency in a target corpus compared to a reference corpus. These statistically significant items, "keywords," reveal what a text or collection of texts is distinctively about, beyond what general English frequency would predict.
The result is a ranked list of items that characterise the target corpus: its aboutness and its distinctive linguistic features.
| Measure | Description |
|---|---|
| Log-likelihood (Dunning 1993) | The most widely used keyness statistic; tests significance of frequency differences; good with low-frequency items |
| Chi-squared | Earlier measure; less reliable with sparse data |
| Odds ratio | Measures effect size rather than significance; how much more likely a word is in the target |
| Kullback-Leibler Divergence | Information-theoretic approach; treats keyness as effect size (Gries 2021) |
Mike Scott's WordSmith Tools (1996 onwards) popularised keyword analysis and remains a standard tool. Scott defined a keyword simply as a word occurring with "unusual frequency" in comparison to a reference corpus.
Both types are informative: negative keywords reveal what a genre avoids as much as what it foregrounds.
| Application | Example |
|---|---|
| Genre analysis | Identifying characteristic vocabulary of academic, journalistic, or legal Genres |
| ESP/EAP materials | Extracting domain-specific vocabulary for specialised courses |
| Textbook evaluation | Checking whether a coursebook's vocabulary matches target Register |
| Learner corpus research | Comparing learner output against native speaker corpora to identify overuse/underuse |
| Test development | Ensuring reading passages contain vocabulary appropriate to the target level |
Keyword analysis identifies what is distinctive but not why. Interpretation requires qualitative analysis of concordance lines. Additionally, results are heavily influenced by the choice of reference corpus; different references produce different keyword lists.