lfmatosm/embedded-topic-model
A package to run embedded topic modelling with ETM. Adapted from the original at: https://github.com/adjidieng/ETM
This helps researchers and data scientists uncover hidden themes and topics within large collections of text documents, such as academic papers, customer reviews, or social media posts. You provide a list of text documents, and it outputs a set of topics, the words most associated with each topic, and which topics are present in each document. This is ideal for anyone analyzing unstructured text to understand its core subjects.
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Use this if you need to identify and characterize underlying topics in a body of text, especially when you want to leverage word relationships (embeddings) for more nuanced topic discovery.
Not ideal if you need a stable, feature-rich platform for experimenting with many different topic models and extensive hyperparameter tuning, in which case a tool like OCTIS might be more suitable.
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Python
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MIT
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Last pushed
Sep 06, 2023
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