01_​Structural_​Topic_​Model_​(STM)_​Example

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This example shows how to adopt the verified components Topic Extractor (STM) and the Topic Assigner (STM).

The main difference with using the Topic Extractor (Parallel LDA) node is that also the document metadata can be provided during training.

The component adopts the R library 'stm' and requires you to install conda to automatically install the R and the required libraries.

Find more info about the R library, the KNIME R Integration and the Verified Components documentation in the links below.

Nodes

• Inject Variables (Data)32 ×
• Table Row to Variable24 ×
• String Manipulation22 ×
• Column Rename20 ×
• RowID19 ×
• Show all 88 nodes

Extensions

• FeatureKNIME Base nodes
• FeatureKNIME Conda Integration
• FeatureKNIME Data Generation
• FeatureKNIME Interactive R Statistics Integration
• FeatureKNIME JavaScript Views
• FeatureKNIME Javasnippet
• FeatureKNIME Math Expression (JEP)
• FeatureKNIME Quick Forms
• FeatureKNIME Textprocessing

Links

• “PoliBlogs08” data set by Eisenstein and Xing 2010

  dl.acm.org/doi/10.5555/1870658.1870782
• An Introduction to the Structural Topic Model (STM)

  burtmonroe.github.io/TextAsDataCourse/Tutorials/IntroSTM.nb.html
• R Package for Structural Topic Models - Roberts, Stewart and Tingley, Journal of Statistical Software (2019)

  cran.r-project.org/web/packages/stm/vignettes/stmVignette.pdf
• R Installation Guide - KNIME Docs

  docs.knime.com/latest/r_installation_guide
• Miniconda Download and Installation - Conda Docs

  docs.conda.io/en/latest/miniconda.html
• Show all 9 links