When scoring new text documents with a trained topic model (LDA() function in r package topic model) the posterior() function gives back topic probabilities for each topic, but also topic probabilities of each word. Simply adding (by hand) these word probabilities doesn't give back the same topic probabilities that the posterior() function gives back. My question is: how are the probabilities per word used to calculate the topic probabilities?


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closed as off-topic by gung Apr 23 at 13:52

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  • $\begingroup$ Presumably, you need to read the documentation for this. As currently phrased, this question seems borderline & at risk of being closed as off topic here. Can you edit this to make it less about how the package works & more about how the underlying methods work? You may also want to add a small example to illustrate your question. $\endgroup$ – gung Apr 22 at 18:43
  • $\begingroup$ I will do that! And you are absolutely right $\endgroup$ – rdatasculptor Apr 22 at 20:21

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