Topic modeling is a technique in natural language processing (NLP) that aims to automatically identify topics in a collection of documents, and to group similar documents together based on their topic. Topic modeling is a form of unsupervised learning, which means that it does not rely on pre-defined categories or labels.

Topic modeling is typically performed using machine learning algorithms, such as Latent Dirichlet Allocation (LDA) or Non-negative Matrix Factorization (NMF), which analyze the co-occurrence patterns of words in the documents and identify latent topics that explain those patterns. The output of a topic modeling algorithm is a set of topics, each represented by a probability distribution over the words in the vocabulary, and a set of topic proportions for each document, which indicate the degree to which the document is related to each topic.

Topic modeling can be used to explore large collections of text data and to discover hidden patterns and relationships between the documents. It can help researchers to identify the main themes and trends in a corpus, to summarize and categorize the documents, and to generate hypotheses for further analysis. Topic modeling is widely used in various domains, such as social sciences, digital humanities, marketing, and information retrieval, among others.