Topic modeling involves algorithms that process text data to discover the abstract "topics" that occur in a collection of documents. One of the most popular algorithms is Latent Dirichlet Allocation (LDA), which assumes that each document is a mixture of a small number of topics and that each word in the document is attributable to one of the document's topics. By identifying these patterns, topic modeling helps to summarize and understand the underlying themes present in large datasets.
