Topic modeling is a type of statistical model used to discover abstract topics within a collection of documents. It helps in identifying patterns and themes by analyzing the co-occurrence of words and phrases. The most common algorithms used for topic modeling include Latent Dirichlet Allocation (LDA) and Non-negative Matrix Factorization (NMF). This technique is widely applied in natural language processing for tasks such as document classification, information retrieval, and summarization. By using topic modeling, researchers and organizations can gain insights into large sets of textual data, facilitating better understanding and decision-making.
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