bertopic

We have hosted the application bertopic in order to run this application in our online workstations with Wine or directly.

Quick description about bertopic:

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: