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文章基本信息

  • 标题:stm: An R Package for Structural Topic Models
  • 本地全文:下载
  • 作者:Margaret E. Roberts ; Brandon M. Stewart ; Dustin Tingley
  • 期刊名称:Journal of Statistical Software
  • 印刷版ISSN:1548-7660
  • 电子版ISSN:1548-7660
  • 出版年度:2019
  • 卷号:91
  • 期号:1
  • 页码:1-40
  • DOI:10.18637/jss.v091.i02
  • 出版社:University of California, Los Angeles
  • 摘要:This paper demonstrates how to use the R package stm for structural topic modeling. The structural topic model allows researchers to flexibly estimate a topic model that includes document-level metadata. Estimation is accomplished through a fast variational approximation. The stm package provides many useful features, including rich ways to explore topics, estimate uncertainty, and visualize quantities of interest.
  • 关键词:structural topic model; text analysis; LDA; stm; R.
  • 其他关键词:structural topic model;text analysis;LDA;stm;R
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