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Showing posts from October, 2023

Bi-Term topic modeling in R

As large language models (LLMs) have become all the rage recently, we can look to small scale modeling again as a useful tool to researchers in the field with strictly defined research questions that limit the use of language parsing and modeling to the bi term topic modeling procedure. In this blog post I discuss the procedure for bi-term topic modeling (BTM) in the R programming language. One indication of when to use the procedure is when there is short text with a large "n" to be parsed. An example of this is using it on twitter applications, and related social media postings. To be sure, such applications of text are becoming harder to harvest from online, but secondary data sources can still yield insightful information, and there are other uses for the BTM outside of twitter that can bring insights into short text, such as from open ended questions in surveys.   Yan et al. (2013) have suggested that the procedure of BTM with its Gibbs sampling procedure handles short t