Topic modeling
Modeling is a methodology that's used to identify a topic and derive hidden patterns exhibited by a text corpus.Topic modeling resembles clustering, as we provide the number of topics as a hyperparameter (similar to the one used in clustering), which happens to be the number of clusters (k-means). Through this, we try to extract the number of topics or texts having some weights assigned to them.
The application of modeling lies in the area of document clustering, dimensionality reduction, information retrieval, and feature selection.
There are multiple ways to perform this, as follows:
- Latent dirichlet allocation (LDA): It's based on probabilistic graphical models
- Latent semantic analysis (LSA): It works on linear algebra (singular value decomposition)
- Non-negative matrix factorization: It's based on linear algebra
We will primarily discuss LDA, which is considered the most popular of all.
LDA is a matrix factorization technique that works on an assumption that documents are formed...
