Topic modeling is a technique in natural language processing that helps identify themes or topics within a collection of texts. By analyzing the words and their patterns, it groups similar documents together, making it easier to understand large datasets. This method is often used in fields like data analysis and machine learning. One popular algorithm for topic modeling is Latent Dirichlet Allocation (LDA), which assumes that documents are mixtures of topics. Each topic is represented by a set of words that frequently appear together. This allows researchers to uncover hidden structures in text data, aiding in tasks like content summarization and information retrieval.