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Identify and visually represent a topic model

Topic detection Text summarization LDA Text processing NLP
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This workflow explores a possible way to identify the optimal number of topics which describe appropiately a corpus. After choosing the best solution, it also creates a visual representation of them in a lower dimensional space by computing a distance between topics.

Used extensions & nodes

Created with KNIME Analytics Platform version 4.6.3 Note: Not all extensions may be displayed.
  • KNIME Base nodes Trusted extension

    KNIME AG, Zurich, Switzerland

    Versions 4.3.2, 4.5.1, 4.6.2

    knime
  • KNIME Interactive R Statistics Integration Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.6.0

    knime
  • KNIME JavaScript Views Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.6.2

    knime
  • KNIME Javasnippet Trusted extension

    KNIME AG, Zurich, Switzerland

    Versions 4.5.0, 4.6.0

    knime
  • KNIME Math Expression (JEP) Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.6.0

    knime
  • KNIME Quick Forms Trusted extension

    KNIME AG, Zurich, Switzerland

    Versions 4.5.0, 4.6.0

    knime
  • KNIME Statistics Nodes Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.5.0

    knime
  • KNIME Textprocessing Trusted extension

    KNIME AG, Zurich, Switzerland

    Versions 4.3.0, 4.6.2

    knime
  • KNIME Textprocessing - Deeplearning4J Integration (64bit only) Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.5.0

    knime
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