Can dummy variables be used to represent space in latent Dirichlet allocation?
I have a set of geocoded textual documents. I would like to use LDA to generate a topic model for the documents. Intuitively, I would think that documents that are near each other spatially are more likely to have the same topic but I don’t know how to represent space in the R packages for topic models.
I thought about binning the documents by space and assigning the bin name as a term that occurs in each document (e.g. all documents from Kenya would be annotated with “Kenya”). Does this violate the model assumptions? Is there any point to doing this? To be honest, I don’t know how LDA works and I’m trying to work through tutorials and play with my dataset to understand it.