Implementation of LDA (Latent Dirichlet Allocation) for classification tasks Until now I have used LDA only for topic modelling. I would like to know which is the simplest implementation of LDA algorithm for classification tasks.
You can use LDA on your training data to build the topic representation of it for example:


(document)entry[1] Label A: (topic 1 has 4 words in document 1)T[1]=4, T[3]=7, T[4]=5..
entry[2] Label C: T[1]=3,T[2]=2...
entry[3] Label A: T[1]=2,T[2]=2,T[3]=5...
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Using this you can build a simple decision tree: T[1]>1 AND T[3]>4 AND (T[2]>1 OR T[4] > 3) ---> Label A

Another approach would be to use: https://en.wikipedia.org/wiki/Dirichlet-multinomial_distribution#A_second_example:_Naive_Bayes_document_clustering