I am new to topic modeling and read about LDA and NMF (Non-negative Matrix Factorization). I understand the training process work. Let's say I have 100 documents and I want to train an LDA for these documents with 10 topics. However, I don't really understand how does this model assign topic to an unseen document?
I used Gensim. After training, I have an LDA trained model and a dictionary with most frequent words. Let's say, I have an unseen new document with the following text:
This is just a test text about topic modeling and LDA.
Can someone explain step by step how a topic distribution is assigned to this new document in terms of algorithmic steps? The same goes for NMF method.