koheiw/seededlda
LDA for semisupervised topic modeling
This R package helps researchers analyze large collections of text documents, like news articles or social media posts, by identifying underlying themes or topics. You provide a list of texts and, optionally, some keywords to guide the topic discovery, and it outputs a breakdown of the main topics present and how much each document discusses them. This is useful for social scientists, market researchers, or anyone needing to make sense of unstructured text data.
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Use this if you need to automatically categorize text documents into topics, especially when you have some prior knowledge about what those topics might be and want to guide the analysis.
Not ideal if you prefer a completely unsupervised approach without any initial keyword guidance for topic discovery, or if you are not comfortable working within the R environment.
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R
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Last pushed
Sep 20, 2025
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