CFTM: Continuous time fractional topic model
This addresses the problem of modeling dynamic topics with long-term correlations in text data, such as economic news, but appears incremental as it builds on existing topic modeling frameworks.
The paper introduces the Continuous Time Fractional Topic Model (cFTM) for dynamic topic modeling, which uses fractional Brownian motion to capture long-term dependencies or roughness in topic and word distributions over time, with empirical validation on economic news articles.
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.