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

• URL: http://arxiv.org/abs/2203.05794v1
• Date: Fri, 11 Mar 2022 08:35:15 GMT
• Title: BERTopic: Neural topic modeling with a class-based TF-IDF procedure
• Authors: Maarten Grootendorst
• Abstract summary: We present BERTopic, a topic model that extends the feasibility of approach topic modeling as a clustering task. BERTopic generates coherent topics and remains competitive across a variety of benchmarks involving classical models and those that follow the more recent clustering approach of topic modeling.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Topic models can be useful tools to discover latent topics in collections of documents. 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. BERTopic generates coherent topics and remains competitive across a variety of benchmarks involving classical models and those that follow the more recent clustering approach of topic modeling.

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