Different Techniques in Topic Modeling: LDA, Mallet LDA, STM & HDP

In natural language processing, topic modeling is a type of statistical modeling that is used to discover abstract topics in a collection of documents. Though there are multiple techniques available in topic modeling implementation, evaluating the models has been challenging due to its unsupervised training process. There is no standard set of output metrics to compare for every corpus. However, it is equally important to identify if the trained model is good or bad and to have the ability to compare different models/techniques. In this blog, we will explore different techniques and…

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Ramya Balakrishnan

Ramya Balakrishnan

Data Scientist at Center for Creative Leadership