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I'm currently working on a Dynamic Topic Modeling project using LdaSeqModel to analyze a dataset consisting of 20,000 online Chinese reviews of tourist attractions. During the modeling process, I've noticed that certain terms appear in time periods where they seemingly shouldn't, and I suspect this may be due to the model's smoothing process. Specifically, terms like "COVID-19" are occurring in time periods before the outbreak of the COVID-19 pandemic, which is unexpected.
Problem description
I'm currently working on a Dynamic Topic Modeling project using LdaSeqModel to analyze a dataset consisting of 20,000 online Chinese reviews of tourist attractions. During the modeling process, I've noticed that certain terms appear in time periods where they seemingly shouldn't, and I suspect this may be due to the model's smoothing process. Specifically, terms like "COVID-19" are occurring in time periods before the outbreak of the COVID-19 pandemic, which is unexpected.
Here is my code
Versions
Windows-10-10.0.22631-SP0
Python 3.8.10 (tags/v3.8.10:3d8993a, May 3 2021, 11:48:03) [MSC v.1928 64 bit (AMD64)]
Bits 64
NumPy 1.24.2
SciPy 1.8.1
gensim 4.3.2
FAST_VERSION 1
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