The idea behind topic model is to discover the relationships between words and phrases. Topic modelling helps us to organize, search, understand and summarize large collections of textual information.
Each keyword term can be considered a document. Topic modelling targets to find a group of topics from a collection of documents that best represents the information in the collection. It can be thought of a way of text mining to obtain keyword attributes.
There are many ways to obtain topic models. The most widely used method is Latent Dirchlet Allocation (LDA). In the LDA model, each document is viewed as a mixture of topics that are present in the corpus. The model proposes that each word in the document is attributable to one.