It is a type of analysis mostly implemented in NLP (natural language processing) tasks and is based on the Dirichlet probability distribution in relation to topics in texts documents. This is the reason for Dirichlet allocation term. Typically term Latent Dirichlet allocation is used as it is also considered in terms of latent variable analysis in relation to words in the text. Most common implementations, machine learning / bayesian networks.
If you mean LDA(Latent Dirichlet Allocation), it is a method used to find topics in a collection of documents. Imagine you have a bunch of articles, and you want to figure out what main themes or topics they cover.
LDA helps by assuming that each document is a mix of different topics, and each topic is a mix of different words. It then tries to uncover these mixes by analyzing the words in the documents.
To put it even more straightforwardly, LDA helps organize and identify the main subjects present in a set of texts without needing prior information about what those subjects might be. It's like letting a computer find the hidden themes in a pile of articles or documents which can be useful.
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