LDAs

LDAs, or Latent Dirichlet Allocation, is a generative probabilistic model used for topic modeling. It assumes that documents are mixtures of topics, and that topics are probability distributions over words. The goal of LDA is to discover these underlying topics from a collection of documents.

The process begins by assuming a prior distribution on the topic mixtures for each document and a prior distribution on the word distributions for each topic. Then, for each word in each document, LDA assumes it was generated by first selecting a topic from the document's topic mixture, and then selecting a word from that topic's word distribution.

LDA iteratively refines these topic and document assignments. By observing the words in the documents, it learns which topics are likely to contain which words and which topics are likely to be present in which documents. This iterative process, often implemented using algorithms like Gibbs sampling or variational inference, allows LDA to uncover the hidden thematic structure within a corpus. The output of LDA is typically a set of topics, each represented by its most probable words, and for each document, a distribution of topics it contains. This is useful for tasks such as document summarization, information retrieval, and content recommendation.