Latent Dirichlet Allocation

Latent Dirichlet Allocation is a generative statistical model that allows sets of observations to be explained by unobserved groups that explain why some parts of the data are similar. In the context of financial text mining, it treats documents as mixtures of topics and topics as mixtures of words.

This algorithm is essential for discovering the hidden thematic structure in large archives of financial reports or governance proposals. It enables analysts to classify complex datasets without needing human intervention for tagging.

By identifying these latent themes, researchers can track how discussions around specific protocols or assets change over time. It provides a mathematical foundation for extracting signal from the noise of massive social media feeds.

This technique is a cornerstone of automated information processing in quantitative finance.