Topic Assigner (STM)

Use the component to apply the model trained with the 'Topic Extractor (STM)' component. See the other component for more information.

This component integrates with the R implementation of Structural Topic Models (STM), following Roberts, Stewart and Tingley, Journal of Statistical Software (2019) (cran.r-project.org/web/packages/stm/vignettes/stmVignette.pdf), via the R library 'stm' (cran.r-project.org/web/packages/stm).

On its first execution the component is set up to automatically install R and all the required libraries. For this to work you need to install Conda (we recommend via "docs.conda.io/en/latest/miniconda.html"). KNIME Analytics Platform can automatically find the default path of where Conda is installed.  You can make sure KNIME Analytics Platform is using the correct path via "File > Preferences > KNIME > Conda".

DISCLAIMER: this component won't work on Apple M1 systems as the 'stm' package is not available for 'osx-arm64' via 'conda-forge' ("anaconda.org/conda-forge/r-stm"). For Apple Intel systems manual installation of additional software might be required after the Conda Environment Propagation node executes. For details visit: docs.knime.com/latest/r_installation_guide

Input ports

1. Type: R Workspace
   R Model
   The R object with the trained model. Use the component "Topic Assigner (STM)" to apply this model to new documents.
2. Type: Table
   Document Table
   Data table with the document collection to analyze in the KNIME Textprocessing column type (use the 'Strings to Document' node first). Each row contains one document. Documents can be pre-processed (stopwords removal, stemming, ...).

Output ports

1. Type: Table
   Document with Assigned Topics
   The document collection with topic assignments and the probability for each document to belong to a certain topic. Such probabilities are taken from the gamma/theta matrix returned by the 'stm_tidiers' R function. Missing values are listed for rows with missing text or selected metadata fields/columns.