thakur-nandan/topic-modeling

This repository contains as intuitive example on topic-modeling using regular LDA, and how GuidedLDA is better than regular LDA

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This helps researchers, analysts, or content strategists understand the main themes within a large collection of text documents. You input a dataset of articles, reviews, or reports, and it outputs the underlying topics present in the text, along with which documents relate to which topics. This is for anyone who needs to make sense of unstructured text data without manually reading every piece.

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Use this if you need to discover and organize hidden thematic structures across many documents, especially when you have some initial idea of the topics you're looking for.

Not ideal if you only have a few short documents or if you need to perform sentiment analysis or named entity recognition rather than topic discovery.

text-analysis content-categorization document-organization information-discovery research-analysis
Stale 6m No Package No Dependents
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Adoption 4 / 25
Maturity 16 / 25
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Stars

7

Forks

5

Language

Jupyter Notebook

License

MIT

Last pushed

Dec 08, 2022

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