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 particularly useful for organizing information and discovering hidden structures in text data. One common algorithm used in Topic Modeling is Latent Dirichlet Allocation (LDA), which assumes that documents are mixtures of topics. Each topic is represented by a distribution of words, allowing researchers to interpret the main themes present in the text. This approach is valuable in fields like data analysis and information retrieval.