ethanhezhao/MetaLDA
The code for MetaLDA in ICDM 2017
This is a topic modeling tool that helps researchers and data scientists understand the underlying themes within a collection of text documents. It takes text documents, optionally with labels and word embeddings, and outputs lists of top words for each identified topic, along with statistical data about the document-topic distributions. It's designed for users who need to uncover structured insights from unstructured text data.
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Use this if you need to perform advanced topic modeling on text datasets and want to leverage additional context from document labels or word characteristics to get more accurate and meaningful topics.
Not ideal if you are looking for a simple, out-of-the-box topic modeling solution that doesn't require prior data preparation into a specific format or interaction via command-line tools.
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Language
Java
License
MIT
Category
Last pushed
Feb 06, 2019
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