Principles of Data Science
Non-negative matrix factorization (NMF) is a mathematical technique used to decompose a non-negative matrix into two lower-dimensional non-negative matrices, typically referred to as factors. This method is particularly useful for extracting latent features and patterns in data, enabling applications such as sentiment analysis and topic modeling, where understanding underlying themes and sentiments in large text datasets is crucial.
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