LDA (Latent Dirichlet Allocation)

Latent Dirichlet Allocation (LDA) is a statistical method used to discover hidden topics within a collection of documents. Imagine it as a way for a computer to analyze a set of texts and identify patterns of words that tend to appear together, revealing underlying themes or subjects. LDA assumes that each document is a mix of several topics, and each topic is characterized by a specific group of words. By doing this, it helps organize large text collections into manageable themes, making it easier to understand the main ideas and categorize content efficiently.