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Topic Models from Reviews

Marketing Analytics Regression R script LDA NLP
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This workflow addresses the problem of extracting and modeling topics from reviews. Block 1 performs the data preparation on review texts. Block 2 optimizes the parameters for the LDA algorithm. Block 3 applies the LDA algorithm with optimized parameters and displays the LDA topic probabilities along with the average number of stars by topic. Block 4 estimates the importance of topics via linear regression (KNIME) and polynomial regression (R). If you use this workflow, please cite: F. Villaroel Ordenes & R. Silipo, “Machine learning for marketing on the KNIME Hub: The development of a live repository for marketing applications”, Journal of Business Research 137(1):393-410, DOI: 10.1016/j.jbusres.2021.08.036.

External resources

  • 10.1016/j.jbusres.2021.08.036

Used extensions & nodes

Created with KNIME Analytics Platform version 4.5.0
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    KNIME Base nodes Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.5.0

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    KNIME Excel Support Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.5.0

  • Go to item
    KNIME Interactive R Statistics Integration Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.5.0

  • Go to item
    KNIME JavaScript Views Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.5.0

  • Go to item
    KNIME Javasnippet Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.5.0

  • Go to item
    KNIME Math Expression (JEP) Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.5.0

  • Go to item
    KNIME Textprocessing Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.5.0

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