Half II: Subject Extraction with BERTopic
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Within the first a part of this collection, I launched you to my artificially created buddy John, who was good sufficient to offer us along with his chats with 5 of the closest folks in his life. We used simply the metadata, equivalent to who despatched messages at what time, to visualise when John met his girlfriend, when he had fights with certainly one of his finest mates and which relations he ought to write to extra typically. When you didn’t learn the primary a part of the collection, you could find it right here.
What we didn’t cowl but however we are going to dive deeper into now’s an evaluation of precise messages. Subsequently, we are going to use the chat between John and Maria to determine the matters they talk about. And naturally, we won’t undergo the messages one after the other and classify them — no, we are going to use the Python library BERTopic to extract the matters that the chats revolve round.
What’s BERTopic?
BERTopic is a subject modeling approach launched by Maarten Grootendorst that makes use of transformer-based embeddings, particularly BERT embeddings, to generate coherent and interpretable matters from massive collections of paperwork. It was designed to beat the constraints of conventional subject modeling approaches like LDA (Latent Dirichlet Allocation), which regularly battle to deal with quick…