nameoften

Nameoften is a coined metric used in corpus linguistics to quantify how frequently a personal name appears within a text or collection of texts. It is designed to capture the prominence of specific names in discourse and can be used to analyze naming practices, cultural focus, or character prominence in fiction.

Calculations for nameoften can be defined in several ways. A common normalization is relative frequency per token: nameoften(N, C) = occurrences of N in C divided by total tokens in C. Another approach is per named-entity mentions: nameoften(N, C) = occurrences of N in C divided by total named-entity mentions in C. These measures can be calculated for individual documents or aggregated across documents and time periods to reveal trends.

Methodology and challenges include the need for accurate named-entity recognition and, for cross-document comparisons, coreference resolution to consolidate different mentions of the same individual. Variants and transliterations must be normalized, and ambiguity arising from common names can bias scores. Disambiguation strategies and cross-language normalization help improve comparability.

Applications of nameoften span sociolinguistic studies of naming conventions, historical corpus analysis, and literary studies that estimate character focus or name prominence. It can also inform marketing analytics by tracking mentions of brands or public figures in media.

Limitations should be acknowledged: the metric depends on preprocessing quality and corpus composition, and it measures frequency rather than sentiment, popularity, or significance beyond occurrence counts.

Example: in a 1,000,000-token corpus with 2,000 occurrences of the name Emma and 50,000 total named-entity mentions, nameoften Emma per token is 0.002, and per named-entity is 0.04.