Topic modeling is a popular text analysis technique. The ultimate goal of topic modeling to find a theme across reviews, and discover hidden topics. Each document in the corpus will be made up of at least one topic, if not multiple topics.
To use a topic modeling technique, you need to provide:
A document-term matrix.
The number of topics you would like the algorithm to pick up.
Here I have used LDA(Latent Dirichlet Allocation), and NTM(Neural Topic Model), and evaluated their performance across few use-cases.