bloomberg/fast-noise-aware-topic-clustering
Research code and scripts used in the Silburt et al. (2021) EMNLP 2021 paper 'FANATIC: FAst Noise-Aware TopIc Clustering'
FANATIC helps you find hidden topics within large collections of text, even when your data is noisy or includes many irrelevant documents. It takes raw text data, like social media posts, and outputs clearly defined topic clusters, along with summaries of what each cluster contains. This is ideal for researchers or analysts who need to make sense of unstructured text.
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Use this if you need to automatically group vast amounts of text into meaningful categories and identify noise or irrelevant content within your dataset.
Not ideal if you need an out-of-the-box solution with a graphical interface for general topic modeling without custom code.
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Language
Python
License
Apache-2.0
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Last pushed
Jul 06, 2023
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