I have a corpus and number of documents with me. I am trying to generate a similarity score between the corpus and each of the available documents using latent dirichlet allocation. The goal is to see how similar the documents are to the corpus. The document that is very similar gets a high similarity score and the one that isn't gets a low similarity score.
I was able to generate a topic model for the corpus
[(0, '0.131*"run" + 0.046*"fast" + 0.045*"woods"'), (1, '0.158*"great" + 0.036*"jump" + 0.029*"sea"'), (2, '0.058*"car" + 0.051*"quality" + 0.030*"engine"')]
and then applied this lda model on the documents to get topic probability distributions
[(0, 0.76487518533846643), (1, 0.19539064775895373), (2, 0.039734166902579866)]
[(0, 0.52023195599405925), (1, 0.41332822829405069), (2, 0.066439815711890018)]
[(0, 0.45468263525907138), (1, 0.5359866943966648)]
[(0, 0.57673377931050251), (1, 0.16880237753053462), (2, 0.25446384315896298)]
[(0, 0.31649406267968366), (1, 0.26830459132208501), (2, 0.41520134599823139)]
But I just need "one number" representing the similarity of document to the corpus and not a probability distribution. Is there any way to generate such a similarity score?
Thanks for your help!