Topic Modeling is an unsupervised machine learning technique that discovers hidden thematic structures in large collections of documents. It automatically identifies topics (clusters of related words) that occur across a corpus without requiring pre-labeled data.

Popular topic modeling algorithms include:

• Latent Dirichlet Allocation (LDA): Assumes documents contain multiple topics in varying proportions
• Non-Negative Matrix Factorization (NMF): Factorizes document-term matrices to discover topics
• Latent Semantic Analysis (LSA): Uses singular value decomposition to find topic patterns

Topic modeling is used for document organization, content recommendation, trend analysis, and exploratory data analysis. It helps researchers and analysts understand large text collections by revealing underlying themes and patterns that might not be immediately apparent.