In a previous blog post, we have discussed different Azure Machine Learning experiments. One of those experiments is the Latent Dirichlet Allocation experiment. This experiment is improved with fewer controls where SQL Transformation was used and it has resulted in removing many other controls.
Latent Dirichlet Allocation or LDA is a statistical technique that was introduced in 2003 from a research paper. LDA is used for topic modelling in text documents. LDA is more often analogue to PCA that we covered before. If you remember in PCA, we used to generate a single value for the existing values in a dataset. LDA will generate a topic for documents by analyzing the content of the document. This technique can be used to cluster documents as well which is an important task in text analytics.
Read the full article at Latent Dirichlet Allocation in Text Analytics
This is ToC for the Azure Machine Learning Series.
Introduction to Azure Machine Learning using Azure ML Studio
LDA can be achieved in the Azure Machine Learning platform as it has specific LDA control. This is the experiment that was created in the Gallery with more than 50 Azure Machine Learning controls.
