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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:

  1. Measure (estimate) the optimal (best) number of topics ⁉️.

  2. 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 ✅

Article Source link

I assembled the code-cells into a single file attached below:

Full Jupiter-Python Code:

Sample Dataset Corpus

Looking forward to your suggestions.

Thanks for your cooperation in advance.

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