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

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.

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• 1 Recent studies have shown the feasibility of approach topic modeling as a clustering task.
• 2 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.
• 3 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.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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