LDA stands for Latent Dirichlet Allocation, and it's one of the widely-used techniques to analyze collections of textual documents.
Note
LDA is an acronym also used by another technique, Linear Discriminant Analysis, which is a supervised method for classification. Pay attention to how LDA is used as there's no connection between these two algorithms.
A full mathematical explanation of LDA would require the knowledge of probabilistic modeling, which is beyond the scope of this practical book. Here, instead, we will give you the most important intuitions behind the model and how to practically apply this model on a massive dataset.
First at all, LDA is used in a branch of data science named text mining, where the focus is on building learners to understand the natural language, for instance, based on textual examples. Specifically, LDA belongs to the category of topic-modeling algorithms as it tries to model the topics included in a document. Ideally, LDA is able to understand whether a document...
