What is LDA topic modeling?
"Latent Dirichlet Allocation (LDA) is an example of topic model and is used to classify text in a document to a particular topic"
https://towardsdatascience.com/topic-modeling-and-latent-dirichlet-allocation-in-python-9bf156893c24
What is topic modeling used for?
"In machine learning and natural language processing, a topic model is a type of statistical model for discovering the abstract "topics" that occur in a collection of documents. Topic modeling is a frequently used text-mining tool for discovery of hidden semantic structures in a text body."
Topic model – Wikipedia
https://en.m.wikipedia.org/wiki/Topic_model
Is LDA supervised or unsupervised?
"Original LDA is unsupervised learning algorithm, while Labeled-LDA and Multi-Grain LDA, another topic model for classification and sentiment analysis, are supervised algorithm."
https://www.quora.com/Is-LDA-latent-dirichlet-allocation-unsupervised-or-supervised-learning
Modeling healthcare data using multiple-channel latent Dirichlet allocation
https://www.sciencedirect.com/science/article/pii/S1532046416000253
Latent Dirichlet Allocation for Classification using Gene Expression Data
https://ieeexplore.ieee.org/document/8251263
A hybrid LDA and genetic algorithm for gene selection and classification of microarray data
https://www.sciencedirect.com/science/article/pii/S0925231210002456
