[Topic-models] Sequentially Updating Dynamic Topic Models as More Data Becomes Available

Nathan.A.Susanj at wellsfargo.com Nathan.A.Susanj at wellsfargo.com
Fri Feb 24 14:31:48 EST 2017


I am new to this list. I work in the Enterprise Analytics group at Wells Fargo, focusing mostly on text data. In one of my recent projects, my team was asked to see if we could detect emerging risks that arise from customer complaint narratives, and I have been exploring using a dynamic topic model on the data as a way of seeing how complaint topics evolve over time at the bank. So far this has proved to be a very interesting application of the DTM.

Recently, however, I have had trouble with trying to think how I might be able to sequentially add on more data to my existing dynamic topic model over time without updating the entire model. Does anyone know if it is possible to "add on" to an existing dynamic model a new time slice without relearning the weights (Betas, alphas and individual LDA model parameters) of the earlier time slices. This would be beneficial in our situation, because we have new complaint data coming in all the time, and it would be nice for model consistencies sake if I could look at how the new complaints cause the topics in my model to evolve without requiring a new model built on the full dataset.

Thanks, I appreciate any feedback or ideas (links to papers, etc.).

Nathan Susanj

Analytic Consultant
Wells Fargo
Enterprise Data & Analytics (EDA)

nathan.a.susanj at wellsfargo.com<mailto:nathan.a.susanj at wellsfargo.com>

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