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UID:pretalx-pyconde-pydata-2025-BJKSGK@pretalx.com
DTSTART;TZID=CET:20250424T150000
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DESCRIPTION:Topic modelling has come a long way\, evolving from traditional
  statistical methods to leveraging advanced embeddings and neural networks
 . Python’s diverse library ecosystem includes tools like Latent Dirichle
 t Allocation (LDA) using gensim\, Top2Vec\, BERTopic\, and Contextualized 
 Topic Models (CTM). This talk evaluates these popular approaches using a d
 ataset of UK climate change policies\, considering use cases relevant to o
 rganisations like DEFRA (Department for Environment\, Food & Rural Affairs
 ). The analysis explores real-time integration\, dynamic topic modelling o
 ver time\, adding new documents\, and retrieving similar ones. Attendees w
 ill learn the strengths\, limitations\, and practical applications of each
  library to make informed decisions for their projects.
DTSTAMP:20260413T210653Z
LOCATION:Hassium
SUMMARY:Decoding Topics: A Comparative Analysis of Python’s Leading Topic
  Modeling Libraries Using Climate C - Dr. Lisa Andreevna Chalaguine
URL:https://pretalx.com/pyconde-pydata-2025/talk/BJKSGK/
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