6 Tips to Optimize an NLP Topic Model for Interpretability

6 Tips to Optimize an NLP Topic Model for Interpretability

In this article we will introduce 6 Tips to Optimize an NLP Topic Model for Interpretability . With so much text outputted on digital platforms, the ability to automatically understand key topic trends can reveal tremendous insight. For example, businesses can benefit from understanding customer conversation trends around their brand and products.

With so much text outputted on digital platforms, the ability to automatically understand key topic trends can reveal tremendous insight. For example, businesses can benefit from understanding customer conversation trends around their brand and products. A common method to pick up key topics is Latent Dirichlet Allocation (LDA). However, outputs are often difficult to interpret for useful insights. We will explore techniques to enhance interpretability.

What is Latent Dirichlet Allocation (LDA)?

Latent Dirichlet Allocation (LDA) is a generative statistical model that helps pick up similarities across a collection of different data parts. In topic modeling, each data part is a word document (e.g. a single review on a product page) and the collection of documents is a corpus (e.g. all users’ reviews for a product page). Similar sets of words occurring repeatedly may likely indicate topics.

nlp topic-modeling data-science machine-learning naturallanguageprocessing

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