Fine-Tune Your Topic Modeling Workflow with BERTopic

A practical guide explains how to fine-tune the BERTopic framework for more focused and interpretable topic modeling. It details adjusting parameters in UMAP for dimensionality reduction and HDBSCAN for clustering to achieve more granular topics. The process emphasizes ensuring reproducibility by setting a random state and caching embeddings to avoid variations. Furthermore, it shows how to enhance topic representations by using n-grams and custom tokenizers instead of default unigrams for better context and clarity.