Description
Latent Dirichlet Allocation (LDA) 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.
How it Works
- LDA represents documents as mixtures of topics.
- Each topic is modeled as a distribution over words.
- LDA assumes that the words of each document are generated by a mixture model corresponding to a random mixture of latent topics.
- The words in each document are assumed to be produced by a two-level generative process.
Benefits
- LDA is a powerful tool for the automatic discovery and recognition of topics in text corpora.
- It provides a simple way to analyze large volumes of unlabeled text.
- LDA can be used to classify text in a document to a particular topic.
- It can also be used to predict topic distribution of a document.
Limitations
- LDA assumes documents are produced from a mixture model, which may not always hold true.
- It also assumes that the order of the words in the document does not matter (bag-of-words assumption), which is not always the case.
- LDA can be quite sensitive to the number of topics parameter, and it’s often not clear what value should be used.
Features
- LDA is a three-level hierarchical Bayesian model where each item of a collection is modeled as a finite mixture over an underlying set of topics.
- Each topic is, in turn, modeled as an infinite mixture over an underlying set of topic probabilities.
- The number of topics is a parameter of the LDA model and must be specified a priori.