Topic Models from reviews

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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.

URL: 10.1016/j.jbusres.2021.08.036 http://10.1016/j.jbusres.2021.08.036

Nodes

• Column Filter7 ×
• Column Rename4 ×
• Math Formula4 ×
• Row Filter4 ×
• Table Creator4 ×
• Show all 44 nodes

Extensions

• FeatureKNIME Base nodes
• FeatureKNIME Excel Support
• FeatureKNIME JavaScript Views
• FeatureKNIME Javasnippet
• FeatureKNIME Math Expression (JEP)
• Show all 6 modules

Links

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