Understanding BERTopic: From Raw Text to Interpretable Topics

Summary

Topic modeling uncovers hidden themes in large document collections. Traditional methods like Latent Dirichlet Allocation rely on word frequency and treat text as bags of words, often missing deeper context and meaning. BERTopic takes a different route, combining transformer embeddings, clustering, and c-TF-IDF to capture semantic relationships between documents. It produces more meaningful, context-aware topics […] The post Understanding BERTopic: From Raw Text to Interpretable Topics appear...