The goal of this project is to examine the experience of art and identity through how these topics are characterized by both artists and institutions. Through extracting core ideas from texts authored by both artists and institutions, pattern analysis can expose what pieces make up the persona of identity that an artist expresses in their work for those whose identity spans across traditional labels and constrained classifications.
The data for this project is manually curated. The collection process begins with art exhibitions dated after 2000 that are curated around the experience of mixed-race and Asian American identity and extracts artists involved whose work is catalogued by major U. S. art insitutions' open access archives. Then, data on these artists is collected from public artist statements and interviews and combined with those institutional records.
Data considered includes records from the following museums, selected from Art Newspaper's list of most visited art museums in 2025 and filtered to museums in the United States that have open access archives or databases available online - numbered by their position in this visitor-ranked list 5. Metropolitan Museum of Art, New York, NY - 5,984,091 visitors 18. National Gallery of Art, Washington, D. C. - 2,847,587 visitors 19. Museum of Modern Art, New York, NY - 2,763,720 visitors 48. Art Insitute of Chicago, Chicago, IL - 1,503,406 visitors 57. J. Paul Getty Center, Los Angeles, CA - 1,323,616 visitors 80. Smithsonian Insitution, Washington, D. C. - 937,716 visitors 86. Whitney Museum of American Art, New York, NY - 885,360 visitors 100. Cleveland Museum of Art, Cleveland, OH - 800,822 visitors
The updated sampling of data collected is available here.
- Collect data (:white_check_mark:) A. Public artist statements, interviews, artwork titles, and institutional biographical text will be the data considered for the analysis
- Clean and standardize data (reusable processes built ✅)
- Modeling (Processes built :whitecheck_mark: _Tuning in progress)
Using nomic-embed-text-v1.5:
A. Split texts by semantic chunks to get 1-3 sentences about same subject
B. Use search_document with terms to filter resulting semantic chunks and keep only those about identity
C. Embed texts
D. Cluster using k-means to group texts by theme, examine and evaluate results
Current progress:
E. Use TF-IDF to get words from all the text in a cluster that "summarize" that cluster 5. Average the text clusters to summarize artists based on their text clustering and embeddings, format to plot on the UI (:whitecheck_mark:) 6. Manually examine clusters and labels to get final cluster labels that will display on UI (**_In progress**)
Summary of all extracted labels, filtered down to those about identity manually:
7. Visually plot clusters and breakdown of themes over time per artists - see prototype here (In progress)
** Note: right now I'm plotting all of the exported clusters with different values for n, this is more exploratory for me to visually understand the results but the final visualization will likely have a couple of versions of these clusters that are combined with the different sources to get a sense of different label/term associations possible with different number of groupings (see below for current progress)
- processing/1_prep-data/clean.ipynb — downloads from Google Sheets CSVs, standardizes mediums, exports to data/downloaded/processed/{date}/
- processing/1_prep-data/merge.ipynb — merges artist/institution word sources
- processing/2_model/text_extraction.ipynb — modeling: chunking, filtering, embedding, clustering, JSON export
- processing/3_ui-clusters/get_artist_points.ipynb — reshaping cluster JSON into per-artist point breakdown
- processing/3_ui-clusters/compute_cluster_overview.ipynb — computing cluster overview averages per artist