BERTopic: Neural topic modeling with a class-based TF-IDF procedure

🤖 Plain-English Summary

Topic models can be useful tools to discover latent topics in collections of documents. More specifically, BERTopic generates document embedding with pre-trained transformer-based language models, clusters these embeddings, and finally, generates topic representations with the class-based TF-IDF procedure.

🔑 Key Findings

• Recent studies have shown the feasibility of approach topic modeling as a clustering task.
• We present BERTopic, a topic model that extends this process by extracting coherent topic representation through the development of a class-based variation of TF-IDF.
• More specifically, BERTopic generates document embedding with pre-trained transformer-based language models, clusters these embeddings, and finally, generates topic representations with the class-based TF-IDF procedure.

🔗 Related Resources