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CFTM: Continuous time fractional topic model

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  • Kei Nakagawa
  • Kohei Hayashi
  • Yugo Fujimoto

Abstract

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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  • Kei Nakagawa & Kohei Hayashi & Yugo Fujimoto, 2024. "CFTM: Continuous time fractional topic model," Papers 2402.01734, arXiv.org, revised Feb 2024.
    • Handle: RePEc:arx:papers:2402.01734
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    References listed on IDEAS

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    1. Angela Ambrosino & Mario Cedrini & John B. Davis & Stefano Fiori & Marco Guerzoni & Massimiliano Nuccio, 2018. "What topic modeling could reveal about the evolution of economics," Journal of Economic Methodology, Taylor & Francis Journals, vol. 25(4), pages 329-348, October.
    2. Jeong, Byeongki & Yoon, Janghyeok & Lee, Jae-Min, 2019. "Social media mining for product planning: A product opportunity mining approach based on topic modeling and sentiment analysis," International Journal of Information Management, Elsevier, vol. 48(C), pages 280-290.
    3. Susana Ferreira & Berna Karali, 2015. "Do Earthquakes Shake Stock Markets?," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-19, July.
    4. Scott Deerwester & Susan T. Dumais & George W. Furnas & Thomas K. Landauer & Richard Harshman, 1990. "Indexing by latent semantic analysis," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 41(6), pages 391-407, September.
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