trajceskijovan/Structural-Topic-Modeling-in-R
Structural Topic Modeling in R (published two articles on Medium). STM, LDA, metadata, NLP.
This project helps financial analysts or market researchers quickly understand the main subjects within large collections of news articles. It takes a dataset of news articles, along with descriptive information like the publisher and date, and groups them into hidden topics. The output provides a clear summary of what each topic is about, the key words associated with it, and which articles discuss it most.
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Use this if you need to extract and analyze recurring themes from a large volume of unstructured text data, such as financial news, and want to leverage existing article metadata to refine your topic detection.
Not ideal if your dataset does not contain relevant metadata that can inform the topic modeling process, or if you need to analyze text in real-time.
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R
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Jul 31, 2021
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