Topic modeling algorithms, such as Latent Dirichlet Allocation (LDA), work by assuming that each document is a mixture of topics and each topic is a mixture of words. The algorithm iteratively adjusts the distribution of topics within documents and words within topics until it finds a stable pattern. The result is a set of topics, each represented by a cluster of words, which can be used to interpret the content of the documents.