What do you need to know about latent Dirichlet allocation?

What do you need to know about latent Dirichlet allocation?

Let’s begin with what the algorithm LDA is all about. Latent Dirichlet Allocation (LDA) is a generative, probabilistic model for a collection of documents, which are represented as mixtures of latent topics, where each topic is characterized by a distribution over words.

How is a Dirichlet distribution used in LDA?

A K-nomial distribution has K possible outcomes (such as in a K-sided dice). In LDA, the Dirichlet is a probability distribution over the K-nomial distributions of topic mixes. But there’s also another Dirichlet distribution used in LDA—a Dirichlet over the words in each topic. So, LDA uses two Dirichlet distributions in its algorithm.

Which is the best way to use LDA?

Each topic represents a set of words. And the goal of LDA is to map all the documents to the topics in a way, such that the words in each document are mostly captured by those imaginary topics. We will systematically go through this method by the end which you will be comfortable enough to use this method on your own.

How is a document represented in a LDA?

LDA states that each documents contains various type of context which relates to various topics. Hence, a document can be represented as collection of various types of topics. Each topic has numerous words with certain range of probabilities. According to LDA each document has its own properties.

What does the lefthand triangle mean in Dirichlet allocation?

In the lefthand triangle the documents are fairly separated, most of them neatly tucked into their corners (this corresponds to a low Dirichlet prior, alpha<1); on the right they are in the middle and represent a more even mix of topics (a higher Dirichlet prior, alpha>1).

What is the lambda value of sklearn’s latent Dirichlet allocation model?

The original paper authors kept lambda in the range of 0.3 to 0.6 (Shirley and Sievert, 2014). The implementation of sklearn’s LatentDirichletAllocation model follows the pattern of most sklearn models. In my notebook, I:

What is the Dirichlet distribution of topics over terms?

Dirichlet distribution of topics over terms: (corresponds to Fig 1.4 and 1.5) for each topic i amongst K topics, what is the probability distribution of words for i. Dirichlet distribution of documents over topics: (corresponds to Fig 1.3) for each document j in our corpus of size M, what is the probability distribution of topics for j.

https://www.youtube.com/watch?v=z2q7LhsnWNg