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  "Title": "Estimation of the Structural Topic and Sentiment-Discourse Model\nfor Text Analysis",
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  "Date": "2025-01-25",
  "Author": "Shawn Mankad [aut, cre], Li Chen [aut]",
  "Authors@R": "c(person(given = \"Shawn\",\nfamily = \"Mankad\",\nrole = c(\"aut\", \"cre\"),\nemail = \"smankad@ncsu.edu\"),\nperson(given = \"Li\",\nfamily = \"Chen\",\nrole = \"aut\",\nemail=\"li.chen@cornell.edu\"))",
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  "Description": "The Structural Topic and Sentiment-Discourse (STS) model\nallows researchers to estimate topic models with document-level\nmetadata that determines both topic prevalence and\nsentiment-discourse. The sentiment-discourse is modeled as a\ndocument-level latent variable for each topic that modulates\nthe word frequency within a topic. These latent topic\nsentiment-discourse variables are controlled by the\ndocument-level metadata. The STS model can be useful for\nregression analysis with text data in addition to topic\nmodeling’s traditional use of descriptive analysis. The method\nwas developed in Chen and Mankad (2024)\n<doi:10.1287/mnsc.2022.00261>.",
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      "title": "A Structural Topic and Sentiment-Discourse Model for Text Analysis",
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        "sts-package"
      ]
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    {
      "page": "estimateRegns",
      "title": "Regression Table Estimation",
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      "title": "Function for Identifying Documents that Load Heavily on a Topic",
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      "title": "Print Top Words that Load Heavily on each Topic",
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        "printTopWords"
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      "page": "sts",
      "title": "Variational EM for the Structural Topic and Sentiment-Discourse (STS) Model",
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        "sts"
      ]
    },
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        "summary.STS"
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