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A category of Unsupervised Machine Learning algorithms that uses clustering techniques to uncover hidden structures in textual data and interpret them as distinct topics. These algorithms analyze large collections of text to identify patterns and group words that frequently occur together, helping to automatically categorize or summarize the content based on underlying themes. Common techniques for topic modeling include Latent Dirichlet Allocation (LDA) and Non-negative Matrix Factorization (NMF).