{
  "_id": "6acbab57fe5cc25786e920be",
  "Package": "sentopics",
  "Type": "Package",
  "Title": "Tools for Joint Sentiment and Topic Analysis of Textual Data",
  "Version": "1.0.1",
  "Authors@R": "c(\nperson(\"Olivier\", \"Delmarcelle\", email = \"delmarcelle.olivier@gmail.com\", role = c(\"aut\", \"cre\"), comment = c(ORCID = \"0000-0003-4347-070X\")),\nperson(\"Samuel\", \"Borms\", email = \"samuel.borms@unine.ch\", role = c(\"ctb\"), comment = c(ORCID = \"0000-0001-9533-1870\")),\nperson(\"Chenghua\", \"Lin\", email = \"chenghua.lin@abdn.ac.uk\", role = \"cph\", comment = \"Original JST implementation\"),\nperson(\"Yulan\", \"He\", email = \"yulan.he@warwick.ac.uk\", role = \"cph\", comment = \"Original JST implementation\"),\nperson(\"Jose\", \"Bernardo\", role = \"cph\", comment = \"Original JST implementation\"),\nperson(\"David\", \"Robinson\", , \"admiral.david@gmail.com\", role = \"cph\", comment = \"Implementation of reorder_within()\"),\nperson(\"Julia\", \"Silge\", , \"julia.silge@gmail.com\", role = \"cph\",\ncomment = c(\"Implementation of reorder_within()\", ORCID = \"0000-0002-3671-836X\"))\n)",
  "Maintainer": "Olivier Delmarcelle <delmarcelle.olivier@gmail.com>",
  "Description": "A framework that joins topic modeling and sentiment\nanalysis of textual data. The package implements a fast Gibbs\nsampling estimation of Latent Dirichlet Allocation (Griffiths\nand Steyvers (2004) <doi:10.1073/pnas.0307752101>) and Joint\nSentiment/Topic Model (Lin, He, Everson and Ruger (2012)\n<doi:10.1109/TKDE.2011.48>). It offers a variety of helpers and\nvisualizations to analyze the result of topic modeling. The\nframework also allows enriching topic models with dates and\nexternally computed sentiment measures. A flexible aggregation\nscheme enables the creation of time series of sentiment or\ntopical proportions from the enriched topic models. Moreover, a\nnovel method jointly aggregates topic proportions and sentiment\nmeasures to derive time series of topical sentiment.",
  "License": "GPL (>= 3)",
  "BugReports": "https://github.com/odelmarcelle/sentopics/issues",
  "URL": "https://github.com/odelmarcelle/sentopics",
  "Encoding": "UTF-8",
  "RcppModules": "model_module",
  "Roxygen": "list(markdown = TRUE)",
  "RoxygenNote": "7.3.3",
  "LazyData": "true",
  "VignetteBuilder": "knitr",
  "Config/pak/sysreqs": "libicu-dev libxml2-dev",
  "Repository": "https://odelmarcelle.r-universe.dev",
  "Date/Publication": "2026-09-10 09:49:46 UTC",
  "RemoteUrl": "https://github.com/odelmarcelle/sentopics",
  "RemoteRef": "HEAD",
  "RemoteSha": "7dcac0ab71030703fd33f9d9b2551ed9b530c7b6",
  "NeedsCompilation": "yes",
  "Packaged": {
    "Date": "2026-10-11 15:14:43 UTC",
    "User": "root"
  },
  "Author": "Olivier Delmarcelle [aut, cre] (ORCID:\n<https://orcid.org/0000-0003-4347-070X>),\nSamuel Borms [ctb] (ORCID: <https://orcid.org/0000-0001-9533-1870>),\nChenghua Lin [cph] (Original JST implementation),\nYulan He [cph] (Original JST implementation),\nJose Bernardo [cph] (Original JST implementation),\nDavid Robinson [cph] (Implementation of reorder_within()),\nJulia Silge [cph] (Implementation of reorder_within(), ORCID:\n<https://orcid.org/0000-0002-3671-836X>)",
  "_user": "odelmarcelle",
  "_type": "src",
  "_file": "sentopics_1.0.1.tar.gz",
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  "_filesize": 1916017,
  "_sha256": "c6d824e91043c11874292b79c1d40aa310ffede390e6799a9daf8df72345f1c0",
  "_expires": "2027-01-19T15:29:25.000Z",
  "_created": "2026-10-11T15:14:43.000Z",
  "_published": "2026-10-11T15:29:27.728Z",
  "_buildurl": "https://github.com/r-universe/odelmarcelle/actions/runs/38150125881",
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    "author": "odelmarcelle <delmarcelle.olivier@gmail.com>",
    "committer": "odelmarcelle <delmarcelle.olivier@gmail.com>",
    "message": "fix: correct rJST sentiment for 1.0.1\n",
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    "cpp",
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    "extra/citation.html",
    "extra/citation.json",
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    "as.JST",
    "as.LDA",
    "as.LDA_lda",
    "as.rJST",
    "as.sentopicsmodel",
    "as.tokens",
    "chains_distances",
    "chains_scores",
    "coherence",
    "compute_PicaultRenault_scores",
    "docvars",
    "fit",
    "fit.sentopicsmodel",
    "grow",
    "JST",
    "LDA",
    "LDAvis",
    "melt",
    "merge_topics",
    "plot_proportion_topics",
    "plot_sentiment_breakdown",
    "plot_sentiment_topics",
    "plot_top_words",
    "proportion_topics",
    "reset",
    "rJST",
    "sentiment_breakdown",
    "sentiment_series",
    "sentiment_topics",
    "sentopics_date",
    "sentopics_date<-",
    "sentopics_labels",
    "sentopics_labels<-",
    "sentopics_sentiment",
    "sentopics_sentiment<-",
    "sentopicsmodel",
    "tokens",
    "top_words"
  ],
  "_datasets": [
    {
      "name": "ECB_press_conferences",
      "title": "Corpus of press conferences from the European Central Bank",
      "object": "ECB_press_conferences",
      "class": [
        "corpus",
        "character"
      ],
      "fields": [],
      "table": false,
      "tojson": true
    },
    {
      "name": "ECB_press_conferences_tokens",
      "title": "Tokenized press conferences",
      "object": "ECB_press_conferences_tokens",
      "class": [
        "tokens"
      ],
      "fields": [],
      "table": false,
      "tojson": false
    },
    {
      "name": "LoughranMcDonald",
      "title": "Loughran-McDonald lexicon",
      "object": "LoughranMcDonald",
      "class": [
        "dictionary2"
      ],
      "fields": [],
      "table": true,
      "tojson": true
    },
    {
      "name": "PicaultRenault",
      "title": "Picault-Renault lexicon",
      "object": "PicaultRenault",
      "class": [
        "data.table",
        "data.frame"
      ],
      "fields": [
        "keyword",
        "ngram",
        "total_class",
        "mp_acco",
        "mp_neut",
        "mp_rest",
        "ec_nega",
        "ec_neut",
        "ec_posi"
      ],
      "rows": 68826,
      "table": true,
      "tojson": true
    },
    {
      "name": "PicaultRenault_data",
      "title": "Regression dataset based on Picault & Renault (2017)",
      "object": "PicaultRenault_data",
      "class": [
        "xts",
        "zoo"
      ],
      "fields": [
        "MRR_t",
        "delta_MRR_t",
        "delta_MRR_t.1",
        "Bloomberg_consensus",
        "Surprise",
        "R_t",
        "R_t.1"
      ],
      "rows": 238,
      "table": true,
      "tojson": false
    }
  ],
  "_help": [
    {
      "page": "sentopics-package",
      "title": "Tools for joining sentiment and topic analysis (sentopics)",
      "topics": [
        "sentopics-package",
        "sentopics"
      ]
    },
    {
      "page": "as.LDA",
      "title": "Conversions from other packages to LDA",
      "topics": [
        "as.LDA",
        "as.LDA.keyATM_output",
        "as.LDA.LDA_Gibbs",
        "as.LDA.LDA_VEM",
        "as.LDA.STM",
        "as.LDA.textmodel_lda",
        "as.LDA_lda"
      ]
    },
    {
      "page": "as.tokens.dfm",
      "title": "Convert back a dfm to a tokens object",
      "topics": [
        "as.tokens.dfm"
      ]
    },
    {
      "page": "chains_distances",
      "title": "Distances between topic models (chains)",
      "topics": [
        "chains_distances"
      ]
    },
    {
      "page": "chains_scores",
      "title": "Compute scores of topic models (chains)",
      "topics": [
        "chains_scores"
      ]
    },
    {
      "page": "coherence",
      "title": "Coherence of estimated topics",
      "topics": [
        "coherence"
      ]
    },
    {
      "page": "compute_PicaultRenault_scores",
      "title": "Compute scores using the Picault-Renault lexicon",
      "topics": [
        "compute_PicaultRenault_scores"
      ]
    },
    {
      "page": "ECB_press_conferences",
      "title": "Corpus of press conferences from the European Central Bank",
      "topics": [
        "ECB_press_conferences"
      ]
    },
    {
      "page": "ECB_press_conferences_tokens",
      "title": "Tokenized press conferences",
      "topics": [
        "ECB_press_conferences_tokens"
      ]
    },
    {
      "page": "fit.sentopicsmodel",
      "title": "Estimate a topic model",
      "topics": [
        "fit.JST",
        "fit.LDA",
        "fit.multi_chains",
        "fit.rJST",
        "fit.sentopicsmodel",
        "grow",
        "grow.JST",
        "grow.LDA",
        "grow.multi_chains",
        "grow.rJST",
        "grow.sentopicsmodel"
      ]
    },
    {
      "page": "get_ECB_press_conferences",
      "title": "Download press conferences from the European Central Bank",
      "topics": [
        "get_ECB_press_conferences"
      ]
    },
    {
      "page": "get_ECB_speeches",
      "title": "Download and pre-process speeches from the European Central Bank",
      "topics": [
        "get_ECB_speeches"
      ]
    },
    {
      "page": "JST",
      "title": "Create a Joint Sentiment/Topic model",
      "concept": [
        "topic models"
      ],
      "topics": [
        "JST"
      ]
    },
    {
      "page": "LDA",
      "title": "Create a Latent Dirichlet Allocation model",
      "concept": [
        "topic models"
      ],
      "topics": [
        "LDA"
      ]
    },
    {
      "page": "LDAvis",
      "title": "Visualize an LDA model using 'LDAvis'",
      "topics": [
        "LDAvis"
      ]
    },
    {
      "page": "LoughranMcDonald",
      "title": "Loughran-McDonald lexicon",
      "topics": [
        "LoughranMcDonald"
      ]
    },
    {
      "page": "melt.sentopicsmodel",
      "title": "Melt for sentopicsmodels",
      "topics": [
        "melt.sentopicsmodel"
      ]
    },
    {
      "page": "merge_topics",
      "title": "Merge topics into fewer themes",
      "topics": [
        "merge_topics"
      ]
    },
    {
      "page": "PicaultRenault",
      "title": "Picault-Renault lexicon",
      "topics": [
        "PicaultRenault"
      ]
    },
    {
      "page": "PicaultRenault_data",
      "title": "Regression dataset based on Picault & Renault (2017)",
      "topics": [
        "PicaultRenault_data"
      ]
    },
    {
      "page": "plot.multi_chains",
      "title": "Plot the distances between topic models (chains)",
      "topics": [
        "plot.multi_chains"
      ]
    },
    {
      "page": "plot.sentopicsmodel",
      "title": "Plot a topic model using Plotly",
      "topics": [
        "plot.sentopicsmodel"
      ]
    },
    {
      "page": "print.sentopicsmodel",
      "title": "Print method for sentopics models",
      "topics": [
        "print.JST",
        "print.LDA",
        "print.rJST",
        "print.sentopicsmodel"
      ]
    },
    {
      "page": "proportion_topics",
      "title": "Compute the topic or sentiment proportion time series",
      "concept": [
        "series functions"
      ],
      "topics": [
        "plot_proportion_topics",
        "proportion_topics"
      ]
    },
    {
      "page": "reset",
      "title": "Re-initialize a topic model",
      "topics": [
        "reset"
      ]
    },
    {
      "page": "rJST",
      "title": "Create a Reverse Joint Sentiment/Topic model",
      "concept": [
        "topic models"
      ],
      "topics": [
        "rJST",
        "rJST.default",
        "rJST.LDA"
      ]
    },
    {
      "page": "sentiment_breakdown",
      "title": "Breakdown the sentiment into topical components",
      "concept": [
        "series functions"
      ],
      "topics": [
        "plot_sentiment_breakdown",
        "sentiment_breakdown"
      ]
    },
    {
      "page": "sentiment_series",
      "title": "Compute a sentiment time series",
      "concept": [
        "series functions"
      ],
      "topics": [
        "sentiment_series"
      ]
    },
    {
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      "title": "Compute time series of topical sentiments",
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