{
  "abstract": "Words and colors are intimately associated in humans, a sign of the close relationship between verbal, linguistic expression and cognition. Word-color association therefore plays an important role in areas of communication such as marketing and literature. Traditional efforts to build a general, scalable, and quantitative mapping of word-color association have often relied on surveys, experiencing difficulties in curating large-scale data sets, enrolling sufficient participants, and excising potential inherent researcher biases in the choice of the words and the colors to be mapped. Even the most recent computational efforts based on Large Language Models (LLMs) do not overcome these problems due to their near-complete lack of transparency in data and methodology that results in low confidence, consistency, and reliability. Here we propose a framework that overcomes these limitations via the use of freely accessible large-scale web image data, and fully transparent and reproducible methods that allow one to build color associations for almost any given word. We also showcase the methodology in action through analyses of color usage in corporate branding and 20th-century Anglo-American poetry. These show that our framework can help form new answers to and perspectives on long-standing questions in communication. Additionally, our framework by design can be readily adopted for use in any language supported by a web image search engine, and its code is freely available in open source; we therefore expect it to contribute towards achieving a universal, multilingual understanding of word-color association.",
  "abstract_shown": true,
  "affiliations": [
    "Graduate School of Culture Technology, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea",
    "School of Computational Sciences, Korea Institute for Advanced Study, Seoul, Republic of Korea"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Sungpil Wang"
    },
    {
      "affs": [
        1
      ],
      "name": "Juyong M. Park"
    }
  ],
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  "cited_by": 0,
  "doi": "10.1371/journal.pone.0360407",
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  ],
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  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
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    ],
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      "/Biology and life sciences/Neuroscience/Cognitive science/Cognitive psychology/Language"
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      "plos",
      "/Biology and life sciences/Neuroscience/Cognitive science/Cognitive psychology/Perception/Sensory perception/Vision/Color vision"
    ],
    [
      "plos",
      "/Biology and life sciences/Neuroscience/Sensory perception/Vision/Color vision"
    ],
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      "plos",
      "/Biology and life sciences/Psychology/Cognitive psychology/Language"
    ],
    [
      "plos",
      "/Biology and life sciences/Psychology/Cognitive psychology/Perception/Sensory perception/Vision/Color vision"
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      "/Physical sciences/Mathematics/Discrete mathematics/Combinatorics/Permutation"
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      "/Physical sciences/Mathematics/Statistics/Similarity measures/Cosine similarity"
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      "/Social sciences/Linguistics/Lexicography/Lexicons"
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      "/Social sciences/Linguistics/Semantics"
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      "plos",
      "/Social sciences/Psychology/Cognitive psychology/Language"
    ],
    [
      "plos",
      "/Social sciences/Psychology/Cognitive psychology/Perception/Sensory perception/Vision/Color vision"
    ]
  ],
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  "pdf_url": "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0360407&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-16",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/9f9z5nt3v84kq.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:23Z",
      "native_id": "10.1371/journal.pone.0360407",
      "source": "plos",
      "url": "https://doi.org/10.1371/journal.pone.0360407"
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  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "plos:neuroscience",
      "subfield": "neuroscience"
    }
  ],
  "title": "Word-color association from large-scale online data: Methodology and applications",
  "title_text": "Word-color association from large-scale online data: Methodology and applications",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:23Z",
  "url": "https://research.draconitic.com/paper/9f9z5nt3v84kq",
  "venue": "PLOS One",
  "versions": []
}