Topic modeling is a machine learning technique used in natural language processing to identify patterns in text data and categorize it into different topics or themes. It involves analyzing a collection of documents to automatically identify common themes or topics based on the words and phrases used. This can help researchers and analysts gain insights into the main ideas and trends present in a large corpus of text data, making it easier to organize, search, and summarize information. Popular algorithms for topic modeling include Latent Dirichlet Allocation (LDA) and Non-negative Matrix Factorization (NMF).