Topic modeling is a type of statistical modeling used in natural language processing (NLP) to discover abstract topics that occur in a collection of documents. The primary goal is to identify the hidden thematic structure within a large set of text. Topic models help in organizing, understanding, and summarizing large datasets of textual information by grouping together words that frequently appear together.
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