I recently got turned on to the idea of Topic modeling by way of Correlation Explanation (CorEx) vis-a-vis this post:

https://medium.com/pew-research-center-decoded/overcoming-the-limitations-of-topic-models-with-a-semi-supervised-approach-b947374e0455

I found an implementation in Python here:

https://github.com/gregversteeg/corex_topic

For those familiar with LDA (Latent Dirichlet Allocation), oftentimes the resulting topics don’t make very much sense. Often the beta probabilities (word-to-topic) are so similar that any classification is arbitrary at best, and more often, simply meaningless.

CorEx provides the ability to “anchor” topics to specific terms, providing a semi-supervised approach to topic modeling. Sounds exciting. Has anyone worked with this algorithm before? Any good results?

Also: has anyone found (or made) an implementation of this CorEx algorithm in R yet?