IDEAS home Printed from https://ideas.repec.org/a/spr/eaiere/v13y2016i2d10.1007_s40844-016-0061-2.html
   My bibliography  Save this article

Constructing of network from topics and their temporal change in the Nikkei newspaper articles

Author

Listed:
  • Shinya Kawata

    (University of Hyogo)

  • Yoshi Fujiwara

    (University of Hyogo)

Abstract

The purpose of this work is to perform the extraction of topics by applying latent Dirichlet allocation (LDA) to a newspaper article data set. Several new topics are generated based on day-by-day reported changes of previous topics in the newspaper articles. When simply reading the newspaper’s articles, it is difficult to notice small changes. In particular, it is important to identify the relationship between changes in society to extract changes for each week (or month) of the structures in the topic group. Illuminating these relationships, we create a network of topics (a topic network) that can track changes in the topic throughout the year using LDA. In addition, we have created a topic network focusing on specific vocabulary items. The proposed method can extract networks of relationships among topics. If we generate the network using this method, we can extract a network focused on specific vocabulary items that have not appeared in previous articles. Therefore, this information retrieval method for topics related to the economy and society can determine the frequency of osccurrence of new words.

Suggested Citation

  • Shinya Kawata & Yoshi Fujiwara, 2016. "Constructing of network from topics and their temporal change in the Nikkei newspaper articles," Evolutionary and Institutional Economics Review, Springer, vol. 13(2), pages 423-436, December.
    • Handle: RePEc:spr:eaiere:v:13:y:2016:i:2:d:10.1007_s40844-016-0061-2
    DOI: 10.1007/s40844-016-0061-2
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s40844-016-0061-2
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s40844-016-0061-2?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Ryohei Hisano & Didier Sornette & Takayuki Mizuno & Takaaki Ohnishi & Tsutomu Watanabe, 2013. "High Quality Topic Extraction from Business News Explains Abnormal Financial Market Volatility," PLOS ONE, Public Library of Science, vol. 8(6), pages 1-12, June.
    2. Ryohei Hisano & Didier Sornette & Takayuki Mizuno & Takaaki Ohnishi & Tsutomu Watanabe, 2012. "High quality topic extraction from business news explains abnormal financial market volatility," Papers 1210.6321, arXiv.org, revised Mar 2013.
    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. T. T. Chen & B. Zheng & Y. Li & X. F. Jiang, 2017. "New approaches in agent-based modeling of complex financial systems," Papers 1703.06840, arXiv.org.
    2. David Lenz & Peter Winker, 2020. "Measuring the diffusion of innovations with paragraph vector topic models," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-18, January.
    3. Andreas Dibiasi & David Iselin, 2021. "Measuring Knightian uncertainty," Empirical Economics, Springer, vol. 61(4), pages 2113-2141, October.
    4. Poza, Carlos & Monge, Manuel, 2020. "A real time leading economic indicator based on text mining for the Spanish economy. Fractional cointegration VAR and Continuous Wavelet Transform analysis," International Economics, Elsevier, vol. 163(C), pages 163-175.
    5. Takayuki Mizuno & Takaaki Ohnishi & Tsutomu Watanabe, 2015. "Novel and topical business news and their impact on stock market activities," UTokyo Price Project Working Paper Series 055, University of Tokyo, Graduate School of Economics.
    6. Tahira, Yoshifumi & Mizuno, Takayuki, 2016. "Amount of news before stock market fluctuations," HIT-REFINED Working Paper Series 45, Institute of Economic Research, Hitotsubashi University.
    7. Melody Y. Huang & Randall R. Rojas & Patrick D. Convery, 2020. "Forecasting stock market movements using Google Trend searches," Empirical Economics, Springer, vol. 59(6), pages 2821-2839, December.
    8. Boubaker, Sabri & Liu, Zhenya & Zhai, Ling, 2021. "Big data, news diversity and financial market crash," Technological Forecasting and Social Change, Elsevier, vol. 168(C).
    9. Thomas J Hwang, 2013. "Stock Market Returns and Clinical Trial Results of Investigational Compounds: An Event Study Analysis of Large Biopharmaceutical Companies," PLOS ONE, Public Library of Science, vol. 8(8), pages 1-8, August.
    10. Ka Kit Tang & Ka Ching Li & Mike K P So, 2021. "Predicting standardized absolute returns using rolling-sample textual modelling," PLOS ONE, Public Library of Science, vol. 16(12), pages 1-28, December.
    11. Yoshifumi Tahira & Takayuki Mizuno, 2016. "Trading strategy of a stock index based on the frequency of news releases for listed companies," Evolutionary and Institutional Economics Review, Springer, vol. 13(2), pages 437-444, December.
    12. Chen, Ting-Ting & Zheng, Bo & Li, Yan & Jiang, Xiong-Fei, 2018. "Information driving force and its application in agent-based modeling," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 496(C), pages 593-601.
    13. Kyoto Yono & Hiroki Sakaji & Hiroyasu Matsushima & Takashi Shimada & Kiyoshi Izumi, 2020. "Construction of Macroeconomic Uncertainty Indices for Financial Market Analysis Using a Supervised Topic Model," JRFM, MDPI, vol. 13(4), pages 1-18, April.
    14. Takayuki Mizuno & Takaaki Ohnishi & Tsutomu Watanabe, 2015. "Novel and topical business news and their impact on stock market activities," CARF F-Series CARF-F-366, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    15. Takayuki Mizuno & Takaaki Ohnishi & Tsutomu Watanabe, 2015. "Novel and topical business news and their impact on stock market activities," Papers 1507.06477, arXiv.org.

    More about this item

    Keywords

    LDA; Machine learning; Bayesian statistics; Network;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General

    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:spr:eaiere:v:13:y:2016:i:2:d:10.1007_s40844-016-0061-2. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.