This is the Windows app named BERTopic whose latest release can be downloaded as v0.14.1.zip. It can be run online in the free hosting provider OnWorks for workstations.
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SCREENSHOTS
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BERTopic
DESCRIPTION
BERTopic is a topic modeling technique that leverages transformers and c-TF-IDF to create dense clusters allowing for easily interpretable topics whilst keeping important words in the topic descriptions. BERTopic supports guided, supervised, semi-supervised, manual, long-document, hierarchical, class-based, dynamic, and online topic modeling. It even supports visualizations similar to LDAvis! Corresponding medium posts can be found here, here and here. For a more detailed overview, you can read the paper or see a brief overview. After having trained our BERTopic model, we can iteratively go through hundreds of topics to get a good understanding of the topics that were extracted. However, that takes quite some time and lacks a global representation. Instead, we can visualize the topics that were generated in a way very similar to LDAvis. By default, the main steps for topic modeling with BERTopic are sentence-transformers, UMAP, HDBSCAN, and c-TF-IDF run in sequence.
Features
- It assumes some independence between these steps which makes BERTopic quite modular
- The main steps for topic modeling with BERTopic are sentence-transformers
- BERTopic has many functions
- Fit the model and predict documents
- Get all topic information
- Generate topic labels
- After having trained your BERTopic model, several are saved within your model
Programming Language
Python
Categories
This is an application that can also be fetched from https://sourceforge.net/projects/bertopic.mirror/. It has been hosted in OnWorks in order to be run online in an easiest way from one of our free Operative Systems.
