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Home Machine Learning

Subject Modelling in Enterprise Intelligence: FASTopic and BERTopic in Code | by Petr Korab | Jan, 2025

Admin by Admin
January 23, 2025
in Machine Learning
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A comparability of two cutting-edge dynamic matter fashions fixing shopper complaints classification train

Petr Korab

Towards Data Science

10 min learn

·

16 hours in the past

Supply: Freepic, Picture by rawpixel.com

Buyer critiques about services present precious details about buyer satisfaction. They supply perception into what must be improved throughout the entire product improvement. Dynamic matter fashions in enterprise intelligence can establish key product qualities and different satisfaction elements, cluster them into classes, and consider how enterprise choices materialized in buyer satisfaction over time. That is extremely precious info not just for product managers.

This text will examine two of the most recent matter fashions to categorise buyer complaints information. BERTopic by Maarten Grootendorst (2022) and the latest FASTopic by Xiaobao Wu et al. (2024) offered finally yr’s NeurIPS, are the present main fashions for matter analytics of buyer information. For these fashions, we’ll discover in Python code:

  • methods to successfully preprocess information
  • methods to prepare a Bigram matter mannequin for buyer grievance evaluation
  • methods to mannequin matter exercise over time.

READ ALSO

Explaining Lineage in DAX | In the direction of Knowledge Science

The Statistics of Token Choice: Logits, Temperature, and Prime-P Walkthrough


A comparability of two cutting-edge dynamic matter fashions fixing shopper complaints classification train

Petr Korab

Towards Data Science

10 min learn

·

16 hours in the past

Supply: Freepic, Picture by rawpixel.com

Buyer critiques about services present precious details about buyer satisfaction. They supply perception into what must be improved throughout the entire product improvement. Dynamic matter fashions in enterprise intelligence can establish key product qualities and different satisfaction elements, cluster them into classes, and consider how enterprise choices materialized in buyer satisfaction over time. That is extremely precious info not just for product managers.

This text will examine two of the most recent matter fashions to categorise buyer complaints information. BERTopic by Maarten Grootendorst (2022) and the latest FASTopic by Xiaobao Wu et al. (2024) offered finally yr’s NeurIPS, are the present main fashions for matter analytics of buyer information. For these fashions, we’ll discover in Python code:

  • methods to successfully preprocess information
  • methods to prepare a Bigram matter mannequin for buyer grievance evaluation
  • methods to mannequin matter exercise over time.
Tags: BERTopicBusinessCodeFASTopicIntelligenceJanKorabModellingPetrTopic

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