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Natural Language Statistics with Latent Dirichlet Allocation by Tommy Jones

Visit https://rstats.ai/gov/ to learn more.

Abstract: Because language is one of the most abundant and information rich data sources that exists, Tommy dreams of a world where we can measure ideas and narratives with the same level of scientific rigor that we use to measure the economy, effects of medical interventions, and other quantitative phenomena. In this talk, Tommy will talk about his work with Latent Dirichlet Allocation to make measurement of language more scientific and how you can access the results of his research through a tidyverse adjacent package, tidylda.

Bio: Tommy is a Senior Member of the Technical Staff at In-Q-Tel, and has previously worked in startups and the US government. He holds a PhD from George Mason University, a masters from Georgetown University, and a bachelors from the College of William and Mary. Tommy is the author of three R packages on CRAN and in his spare time he loves mixing cocktails. He is a Marine Corps veteran and currently lives with his two cats who would like you to know it's time to feed them.

Twitter:   / thos_jones  

Presented at the 2022 Government & Public Sector R Conference (December 1, 2022)