I'd like to combine LDA (latent dirichlet allocation) and logistic regression meaning I'd like to use LDA to generate millions of documents and establish a likelihood to estimate latent variables in the data and simultaneously in the likelihood I also incorporate the classification problem such as description given in the following:
- word A and B are both in a single document but they belong to different topics (complement).
- word A is in a document but word B is in another document while both having the same topics (substitute).
- If it is possible, weight each word based on a vector of features which might be able to capture some degrees of similarities between words.
Can anybody suggest a way to formulate this problem?
I have another question if we know priori the topics and the words belong to each topic e.g. all the words in the dictionary, does it make sense to still use LDA to generate the document and then train them based on above mentioned problem?