{
  "_id": "6ac208ad773a0037682c0d3b",
  "Package": "oolong",
  "Title": "Create Validation Tests for Automated Content Analysis",
  "Version": "0.7.0",
  "Authors@R": "c(person(given = \"Chung-hong\", family = \"Chan\", role = c(\"aut\", \"cre\"), email = \"chainsawtiney@gmail.com\", comment = c(ORCID = \"0000-0002-6232-7530\")),\nperson(given = \"Marius\", family = \"Sältzer\", role = c(\"aut\"), email = \"msaeltze@mail.uni-mannheim.de\", comment = c(ORCID = \"0000-0002-8604-4666\")))",
  "Description": "Intended to create standard human-in-the-loop validity\ntests for typical automated content analysis such as topic\nmodeling and dictionary-based methods. This package offers a\nstandard workflow with functions to prepare, administer and\nevaluate a human-in-the-loop validity test. This package\nprovides functions for validating topic models using word\nintrusion, topic intrusion (Chang et al. 2009,\n<https://papers.nips.cc/paper/3700-reading-tea-leaves-how-humans-interpret-topic-models>)\nand word set intrusion (Ying et al. 2021)\n<doi:10.1017/pan.2021.33> tests. This package also provides\nfunctions for generating gold-standard data which are useful\nfor validating dictionary-based methods. The default settings\nof all generated tests match those suggested in Chang et al.\n(2009) and Song et al. (2020)\n<doi:10.1080/10584609.2020.1723752>.",
  "License": "LGPL (>= 2.1)",
  "Encoding": "UTF-8",
  "URL": "https://gesistsa.github.io/oolong/,\nhttps://github.com/gesistsa/oolong",
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  "RoxygenNote": "7.3.1",
  "BugReports": "https://github.com/gesistsa/oolong/issues",
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  "Config/Needs/website": "gesistsa/tsatemplate",
  "Config/pak/sysreqs": "cmake make libicu-dev libuv1-dev libxml2-dev\nzlib1g-dev",
  "Repository": "https://gesistsa.r-universe.dev",
  "Date/Publication": "2026-05-07 10:26:24 UTC",
  "RemoteUrl": "https://github.com/gesistsa/oolong",
  "RemoteRef": "HEAD",
  "RemoteSha": "35ba20da531a8dc537421400b7f1755ee97154d0",
  "NeedsCompilation": "no",
  "Packaged": {
    "Date": "2026-10-04 07:47:58 UTC",
    "User": "root"
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  "Author": "Chung-hong Chan [aut, cre] (ORCID:\n<https://orcid.org/0000-0002-6232-7530>),\nMarius Sältzer [aut] (ORCID: <https://orcid.org/0000-0002-8604-4666>)",
  "Maintainer": "Chung-hong Chan <chainsawtiney@gmail.com>",
  "_user": "gesistsa",
  "_type": "src",
  "_file": "oolong_0.7.0.tar.gz",
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  "_created": "2026-10-04T07:47:58.000Z",
  "_published": "2026-10-04T08:05:01.310Z",
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    "validation"
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    "name": "Transparent Social Analytics",
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    "description": "Open Science Tools and more maintained by Transparent Social Analytics Team, GESIS"
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    "extra/readme.md"
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      "date": "2020-03-21"
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    },
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    },
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    "clone_oolong",
    "create_oolong",
    "deploy_oolong",
    "export_oolong",
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    "revert_oolong",
    "summarise_oolong",
    "summarize_oolong",
    "ti",
    "update_oolong",
    "wi",
    "witi",
    "wsi"
  ],
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      "title": "Abstracts of communication journals dataset",
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        "tbl",
        "data.frame"
      ],
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      ],
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      "table": true,
      "tojson": true
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      ],
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      "table": false,
      "tojson": false
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      "title": "Abstracts of communication journals dataset",
      "object": "abstracts_dictionary",
      "class": [
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      ],
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      "tojson": true
    },
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      "class": [
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        "list"
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      "fields": [],
      "table": false,
      "tojson": false
    },
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      "title": "AFINN dictionary",
      "object": "afinn",
      "class": [
        "dictionary2"
      ],
      "fields": [],
      "table": true,
      "tojson": true
    },
    {
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      "title": "Naive Bayes model trained on 20 newsgroups data",
      "object": "newsgroup_nb",
      "class": [
        "textmodel_nb",
        "textmodel",
        "list"
      ],
      "fields": [],
      "table": false,
      "tojson": false
    },
    {
      "name": "trump2k",
      "title": "Trump's tweets dataset",
      "object": "trump2k",
      "class": [
        "character"
      ],
      "fields": [],
      "table": false,
      "tojson": true
    }
  ],
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      "title": "Abstracts of communication journals dataset",
      "topics": [
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        "abstracts_dfm",
        "abstracts_dictionary"
      ]
    },
    {
      "page": "abstracts_seededlda",
      "title": "Topic models trained with the abstracts dataset.",
      "topics": [
        "abstracts_btm",
        "abstracts_seededlda"
      ]
    },
    {
      "page": "afinn",
      "title": "AFINN dictionary",
      "topics": [
        "afinn"
      ]
    },
    {
      "page": "check_oolong",
      "title": "Check whether the oolong needs to be updated",
      "topics": [
        "check_oolong"
      ]
    },
    {
      "page": "clone_oolong",
      "title": "Clone an oolong object",
      "topics": [
        "clone_oolong"
      ]
    },
    {
      "page": "create_oolong",
      "title": "Generate an oolong test",
      "topics": [
        "create_oolong",
        "gs",
        "ti",
        "wi",
        "witi",
        "wsi"
      ]
    },
    {
      "page": "deploy_oolong",
      "title": "Deploy an oolong test",
      "topics": [
        "deploy_oolong"
      ]
    },
    {
      "page": "export_oolong",
      "title": "Export a deployable Shiny app from an oolong object into a directory",
      "topics": [
        "export_oolong"
      ]
    },
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      "topics": [
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    },
    {
      "page": "print.oolong_gold_standard",
      "title": "Print oolong gold standard object",
      "topics": [
        "print.oolong_gold_standard"
      ]
    },
    {
      "page": "print.oolong_summary",
      "title": "Print and plot oolong summary",
      "topics": [
        "plot.oolong_summary",
        "print.oolong_summary"
      ]
    },
    {
      "page": "revert_oolong",
      "title": "Obtain a locked oolong from a downloaded data file",
      "topics": [
        "revert_oolong"
      ]
    },
    {
      "page": "summarize_oolong",
      "title": "Summarize oolong objects",
      "topics": [
        "summarise_oolong",
        "summarize_oolong"
      ]
    },
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      "title": "Trump's tweets dataset",
      "topics": [
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      "title": "Update an oolong object to the latest version",
      "topics": [
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      "headings": [
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        "Requirement #2: Keep your data frame",
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        "Oolong's support for BTM"
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      "created": "2020-07-01 16:44:10",
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      "filename": "deploy.html",
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      "headings": [
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        "Suggested workflow",
        "About the p-values",
        "About Biterm Topic Model",
        "About Naive Bayes",
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