I'm using NMF model for topic modeling. I am able to extract topics from my dataset using the NMF model. Next, I ran it on a test data, for it to predict its topic. I got the following result:[[ 0.          0.15796562  0.00465055  0.0019166   0.          0.          0.   0.00123596  0.          0.          0.          0.00089975  0.09570385   0.00034477  0.          0.00216156  0.00050439  0.00066779  0.00616767   0.00024474  0.00497631  0.          0.          0.00121324  0.          0.   0.          0.          0.00323596  0.          0.00301747  0.          0.   0.00225842  0.08339719  0.          0.          0.00233301  0.          0.   0.          0.00803143  0.          0.          0.          0.          0.   0.          0.00440178  0.        ]]This is just for 1 article, (I created 50 topics using the NMF model) so I understand that each number in this numpy array represents the relevance of each of the 50 topics with this article. But what I don't understand is what are these numbers really? (I mean are these probability scores or something else?)
What does the Non Negative Matrix Factorization (NMF) matrix represent?
python;nlp;topic model;lda