I'm trying to build an Orange workflow to perform LDA topic modeling for analyzing a text corpus (.CSV dataset).
Unfortunately, the LDA widget in Orange lacks for advanced settings when comparing it with traditional coding in R or Python, which are commonly used for such purposes.
Accordingly, I would inquire about how to use Orange to:
Measure (estimate) the optimal (best) number of topics ⁉️.
Measuring topic-coherence score in LDA Topic Model in order to evaluate the quality of the extracted topics and their correlation relationships (if any) for extracting useful information ⁉️.
Is there a simple way that can accomplish these tasks in Orange ⁉️.
The following link provides the traditional solution for calculating the topic coherence score using Jupiter-Python as pre-explained ✅
I assembled the code-cells into a single file attached below:
Looking forward to your suggestions.
Thanks for your cooperation in advance.