gregversteeg/corex_topic
Hierarchical unsupervised and semi-supervised topic models for sparse count data with CorEx
This tool helps you uncover key themes and topics within large collections of text documents, like news articles, customer feedback, or research papers. You provide your documents and an optional list of important keywords, and it outputs a set of topics, the words most associated with each, and which documents belong to each topic. It's designed for researchers, analysts, or anyone who needs to make sense of unstructured text data without manually reading every piece.
640 stars. No commits in the last 6 months.
Use this if you need to understand the main subjects being discussed across many documents and want to guide the topic discovery process using specific words or phrases you already know are important.
Not ideal if you're looking for a simple keyword extraction tool or don't have sparse count data as input.
Stars
640
Forks
118
Language
Python
License
Apache-2.0
Category
Last pushed
Mar 22, 2021
Commits (30d)
0
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