CFTM: Continuous time fractional topic model
CoRR(2024)
摘要
In this paper, we propose the Continuous Time Fractional Topic Model (cFTM),
a new method for dynamic topic modeling. This approach incorporates fractional
Brownian motion (fBm) to effectively identify positive or negative correlations
in topic and word distribution over time, revealing long-term dependency or
roughness. Our theoretical analysis shows that the cFTM can capture these
long-term dependency or roughness in both topic and word distributions,
mirroring the main characteristics of fBm. Moreover, we prove that the
parameter estimation process for the cFTM is on par with that of LDA,
traditional topic models. To demonstrate the cFTM's property, we conduct
empirical study using economic news articles. The results from these tests
support the model's ability to identify and track long-term dependency or
roughness in topics over time.
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