Large text datasets tend to be composed of many different topics. For example, Wikipedia contains text about animals, sports, medicine, and many others. We can use clusters in vector space to extract ...

We are thrilled to announce a significant update to the BERTopic Python library, expanding its capabilities and further streamlining the workflow for topic modelling enthusiasts and practitioners.

Modeling topics in short texts presents significant challenges due to feature sparsity, particularly when analyzing content generated by large-scale online users. This sparsity can substantially ...

After training the model, you can access the size of topics in descending order.Topic -1 is the largest and it refers to outliers tweets that do not assign to any topics generated. In this case, we ...