Text Mining Art History Dataset for the Research Project Metadata Culture

A Text Mining Art History Dataset produced by Amanda Wasielewski within this research project. The research for this dataset was conducted within the project Sharing the Visual Heritageat The Department of Culture and Aesthetics, Stockholm University, funded by the Swedish Research Council (No 2018-06057).

Dataset Summary/Readme

Computational methods such as text mining have become increasingly popular in the humanities over the last ten years, and art history is no exception. The aim of this study was to apply one digital humanities (DH) method—text mining—to a representative selection of art history literature in order to understand historiographic trends in the field today. Three sets of journal articles published over the last decade were compiled: (1) articles published in Art History (2) articles published in Art Journal and (3) articles associated with the sub-field of digital art history (DAH) published in the International Journal of Digital Art History and Visual Resources. The text mining operations performed were designed to extract lists of most frequently appearing words in the body text of each sample set as well as lists of which authors were most frequently cited in the footnotes and bibliography of articles.

This dataset is created by Amanda Wasielewski, postdoctoral fellow in art history and participant in the research project Metadata Culture.

Data Report Text Mining Art History 2019-2020 docx, 218.9 kB. (218 Kb)

Venn Diagrams summary pdf, 309.1 kB. (309 Kb)

Results

Top Words/Authors lists

1a bodytext total frequency pdf, 117.1 kB. (117 Kb)

1b bodytext frequency out of articles pdf, 147.8 kB. (147 Kb)

2b bibliography frequency out of articles pdf, 77.6 kB. (77 Kb)

Top Authors in Bibliographies