Article: Latent Dirichlet Allocation in Text Analytics

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.

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Data Cleansing in Azure Machine Learning

Prediction in Azure Machine Learning

Feature Selection in Azure Machine Learning

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Prediction with Regression in Azure Machine Learning

Prediction with Classification in Azure Machine Learning

Comparing models in Azure Machine Learning

Cross Validation in Azure Machine Learning

Clustering in Azure Machine Learning

Tune Model Hyperparameters for Azure Machine Learning models

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Designing Recommender Systems in Azure Machine Learning

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Filter Based Feature Selection in Text Analytics