contefranz/OpTop
Optimal topic identification from a pool of Latent Dirichlet Allocation models
When analyzing large text collections, researchers often use topic modeling to discover underlying themes. This tool helps you statistically determine the most accurate number of topics for your analysis, rather than relying on guesswork. It takes a collection of your topic models and outputs the optimal topic count, ideal for academics or data scientists working with textual data.
Use this if you need a rigorous, statistical method to identify the best number of topics when performing Latent Dirichlet Allocation (LDA) on a text corpus.
Not ideal if you are looking for a tool to perform the initial topic modeling itself, or if you prefer heuristic approaches over statistical testing for topic model validation.
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R
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Last pushed
Feb 16, 2026
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