IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2402.01734.html
   My bibliography  Save this paper

CFTM: Continuous time fractional topic model

Author

Listed:
  • 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.

Suggested Citation

  • 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
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2402.01734
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Leonardo Cei & Edi Defrancesco & Gianluca Stefani, 2022. "What topic modelling can show about the development of agricultural economics: evidence from the Journal Citation Report category top journals," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 49(2), pages 289-330.
    2. Irina Wedel & Michael Palk & Stefan Voß, 2022. "A Bilingual Comparison of Sentiment and Topics for a Product Event on Twitter," Information Systems Frontiers, Springer, vol. 24(5), pages 1635-1646, October.
    3. Hugo S. Gonçalves & Sérgio Moro, 2023. "On the economic impacts of COVID‐19: A text mining literature analysis," Review of Development Economics, Wiley Blackwell, vol. 27(1), pages 375-394, February.
    4. Mohammed Salem Binwahlan, 2023. "Polynomial Networks Model for Arabic Text Summarization," International Journal of Research and Scientific Innovation, International Journal of Research and Scientific Innovation (IJRSI), vol. 10(2), pages 74-84, February.
    5. Curci, Ylenia & Mongeau Ospina, Christian A., 2016. "Investigating biofuels through network analysis," Energy Policy, Elsevier, vol. 97(C), pages 60-72.
    6. Chao Wei & Senlin Luo & Xincheng Ma & Hao Ren & Ji Zhang & Limin Pan, 2016. "Locally Embedding Autoencoders: A Semi-Supervised Manifold Learning Approach of Document Representation," PLOS ONE, Public Library of Science, vol. 11(1), pages 1-20, January.
    7. Jussi T. S. Heikkila, 2020. "Classifying economics for the common good: Connecting sustainable development goals to JEL codes," Papers 2004.04384, arXiv.org.
    8. Maksym Polyakov & Morteza Chalak & Md. Sayed Iftekhar & Ram Pandit & Sorada Tapsuwan & Fan Zhang & Chunbo Ma, 2018. "Authorship, Collaboration, Topics, and Research Gaps in Environmental and Resource Economics 1991–2015," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 71(1), pages 217-239, September.
    9. Ding, Ying, 2011. "Community detection: Topological vs. topical," Journal of Informetrics, Elsevier, vol. 5(4), pages 498-514.
    10. Klaus Gugler & Florian Szücs & Ulrich Wohak, 2023. "Start-up Acquisitions, Venture Capital and Innovation: A Comparative Study of Google, Apple, Facebook, Amazon and Microsoft," Department of Economics Working Papers wuwp340, Vienna University of Economics and Business, Department of Economics.
    11. Juan Shi & Kin Keung Lai & Ping Hu & Gang Chen, 2018. "Factors dominating individual information disseminating behavior on social networking sites," Information Technology and Management, Springer, vol. 19(2), pages 121-139, June.
    12. Zhen-Yu Chen & Xin-Li Liu & Li-Ping Yin, 2023. "Data-driven product configuration improvement and product line restructuring with text mining and multitask learning," Journal of Intelligent Manufacturing, Springer, vol. 34(4), pages 2043-2059, April.
    13. Ganesh Dash & Chetan Sharma & Shamneesh Sharma, 2023. "Sustainable Marketing and the Role of Social Media: An Experimental Study Using Natural Language Processing (NLP)," Sustainability, MDPI, vol. 15(6), pages 1-16, March.
    14. Mohamed M. Mostafa, 2023. "A one-hundred-year structural topic modeling analysis of the knowledge structure of international management research," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(4), pages 3905-3935, August.
    15. Paola Cerchiello & Giancarlo Nicola, 2018. "Assessing News Contagion in Finance," Econometrics, MDPI, vol. 6(1), pages 1-19, February.
    16. Shr-Wei Kao & Pin Luarn, 2020. "Topic Modeling Analysis of Social Enterprises: Twitter Evidence," Sustainability, MDPI, vol. 12(8), pages 1-20, April.
    17. Gissler, Stefan & Oldfather, Jeremy & Ruffino, Doriana, 2016. "Lending on hold: Regulatory uncertainty and bank lending standards," Journal of Monetary Economics, Elsevier, vol. 81(C), pages 89-101.
    18. Wittek, Peter, 2013. "Two-way incremental seriation in the temporal domain with three-dimensional visualization: Making sense of evolving high-dimensional datasets," Computational Statistics & Data Analysis, Elsevier, vol. 66(C), pages 193-201.
    19. Alina Evstigneeva & Mark Sidorovskiy, 2021. "Assessment of Clarity of Bank of Russia Monetary Policy Communication by Neural Network Approach," Russian Journal of Money and Finance, Bank of Russia, vol. 80(3), pages 3-33, September.
    20. Arno de Caigny & Kristof Coussement & Koen W. de Bock & Stefan Lessmann, 2019. "Incorporating textual information in customer churn prediction models based on a convolutional neural network," Post-Print hal-02275958, HAL.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2402.01734. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.