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  "Title": "Supervised Semantic Differential",
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  "Description": "Interpretable, embedding-based analysis of concept meaning\nin text. Builds per-document concept vectors from local\ncontexts around seed words, learns a semantic gradient that\npredicts a numeric outcome or separates groups, and interprets\nit through the nearest neighbours of each pole, clusters of\nthose neighbours, text snippets and per-document scores. The\nmethod is described in Plisiecki, Lenartowicz, Pokropek,\nMałyska and Flakus (2025) <doi:10.31234/osf.io/gvrsb_v1>. A\nport of the 'Python' package 'ssdiff'.",
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  "URL": "https://github.com/pawlenartowicz/ssdiff-r",
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      "title": "Words, clusters and snippets of a result",
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        "clusters",
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        "cluster_words",
        "snippets",
        "words"
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        "doc_text"
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    {
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      "title": "Fit PLS gradients for several outcomes",
      "topics": [
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    {
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    {
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      "topics": [
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      ]
    },
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      "title": "Install the plskit and fastglmm backends",
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    },
    {
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      "title": "Tokens of a lexicon suggestion",
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      ]
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      "title": "Load a wordpack",
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    },
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      "title": "Outcome loadings of a multi-outcome fit",
      "topics": [
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      "topics": [
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        "save_page"
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        "predict.ssd_pls",
        "predict.ssd_result"
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    },
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    {
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      "topics": [
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    },
    {
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        "print.ssd_groups",
        "print.ssd_multipls",
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        "confint.ssd_pca_ols",
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        "fitted.ssd_pca_ols",
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