Topic Models from Reviews

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