IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i14p8085-d597574.html
   My bibliography  Save this article

A Survey of Research on Data Analytics-Based Legal Tech

Author

Listed:
  • So-Hui Park

    (Department of Industrial and Management Systems Engineering, Dong-A University, Busan 49315, Korea)

  • Dong-Gu Lee

    (Department of Industrial and Management Systems Engineering, Dong-A University, Busan 49315, Korea)

  • Jin-Sung Park

    (Department of Industrial and Management Systems Engineering, Dong-A University, Busan 49315, Korea)

  • Jun-Woo Kim

    (Department of Industrial and Management Systems Engineering, Dong-A University, Busan 49315, Korea)

Abstract

Data analytics provides important tools and methods for processing the data generated during legal services. This paper aims to provide a systematic survey of the research papers on the application of quantitative data analytics algorithms in the legal domain. To this end, relevant research papers were collected and used to analyze topics and trends of research on data analytics-based Legal Tech. The key findings of this paper are as follows. Firstly, the number of research papers about Legal Tech has increased dramatically recently. Secondly, the application of supervised learning techniques to legal judgment data is a very popular approach in this research area. Thirdly, preprocessing legal documents is a very important procedure as many legal documents exist in text form. Fourthly, artificial neural networks and their variations are widely used in research on data analytics-based Legal Tech. Fifthly, data analytics-based Legal Tech is a multidisciplinary research topic related to computer science and social science, etc.

Suggested Citation

  • So-Hui Park & Dong-Gu Lee & Jin-Sung Park & Jun-Woo Kim, 2021. "A Survey of Research on Data Analytics-Based Legal Tech," Sustainability, MDPI, vol. 13(14), pages 1-24, July.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:14:p:8085-:d:597574
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/14/8085/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/13/14/8085/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Christopher C. Yang & Johnny Luk, 2003. "Automatic generation of English/Chinese thesaurus based on a parallel corpus in laws," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 54(7), pages 671-682, May.
    2. Daniel Martin Katz & Michael J Bommarito II & Josh Blackman, 2017. "A general approach for predicting the behavior of the Supreme Court of the United States," PLOS ONE, Public Library of Science, vol. 12(4), pages 1-18, April.
    3. Mauricio Herrera & Cristian Candia & Diego Rivera & Douglas Aitken & Daniel Brieba & Camila Boettiger & Guillermo Donoso & Alex Godoy-Faúndez, 2019. "Understanding water disputes in Chile with text and data mining tools," Water International, Taylor & Francis Journals, vol. 44(3), pages 302-320, April.
    4. Yen‐Liang Chen & Yi‐Hung Liu & Wu‐Liang Ho, 2013. "A text mining approach to assist the general public in the retrieval of legal documents," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 64(2), pages 280-290, February.
    5. Qian Hongdao & Sughra Bibi & Asif Khan & Lorenzo Ardito & Muhammad Bilawal Khaskheli, 2019. "Legal Technologies in Action: The Future of the Legal Market in Light of Disruptive Innovations," Sustainability, MDPI, vol. 11(4), pages 1-19, February.
    6. Marie‐Francine Moens & Caroline Uyttendaele & Jos Dumortier, 1999. "Abstracting of legal cases: The potential of clustering based on the selection of representative objects," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 50(2), pages 151-161.
    7. Sotarat Thammaboosadee & Bunthit Watanapa, 2013. "Identification Of Criminal Case Diagnostic Issues: A Modular Ann Approach," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 12(03), pages 523-546.
    8. Yen-Liang Chen & Yi-Hung Liu & Wu-Liang Ho, 2013. "A text mining approach to assist the general public in the retrieval of legal documents," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 64(2), pages 280-290, February.
    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. I-Cheng Chang & Tai-Kuei Yu & Yu-Jie Chang & Tai-Yi Yu, 2021. "Applying Text Mining, Clustering Analysis, and Latent Dirichlet Allocation Techniques for Topic Classification of Environmental Education Journals," Sustainability, MDPI, vol. 13(19), pages 1-20, September.
    2. Elisa Blanco & Guillermo Donoso & Pablo Camus, 2023. "Water Conflicts in Chile: Have We Learned Anything from Colonial Times?," Sustainability, MDPI, vol. 15(19), pages 1-17, September.
    3. Anthony Niblett, 2018. "Regulatory Reform in Ontario: Machine Learning and Regulation," C.D. Howe Institute Commentary, C.D. Howe Institute, issue 507, March.
    4. Prof. Dr.Sejdi Rexhepi & Mjellma Kadriu, 2018. "The Importance of Resource Assessment for Entrepreneurship and Local Economic Development in Kosovo," European Journal of Economics and Business Studies Articles, Revistia Research and Publishing, vol. 4, January -.
    5. Amedeo Santosuosso & Giulia Pinotti, 2020. "Bottleneck or Crossroad? Problems of Legal Sources Annotation and Some Theoretical Thoughts," Stats, MDPI, vol. 3(3), pages 1-20, September.
    6. Alain Marciano & Antonio Nicita & Giovanni Battista Ramello, 2020. "Big data and big techs: understanding the value of information in platform capitalism," European Journal of Law and Economics, Springer, vol. 50(3), pages 345-358, December.
    7. Ulenaers Jasper, 2020. "The Impact of Artificial Intelligence on the Right to a Fair Trial: Towards a Robot Judge?," Asian Journal of Law and Economics, De Gruyter, vol. 11(2), pages 1, August.
    8. Sughra Bibi & Asif Khan & Hongdao Qian & Achille Claudio Garavelli & Angelo Natalicchio & Paolo Capolupo, 2020. "Innovative Climate, a Determinant of Competitiveness and Business Performance in Chinese Law Firms: The Role of Firm Size and Age," Sustainability, MDPI, vol. 12(12), pages 1-24, June.
    9. Zhong, Weifeng & Chan, Julian, 2020. "Predicting Authoritarian Crackdowns: A Machine Learning Approach," Working Papers 10464, George Mason University, Mercatus Center.
    10. Bruno Mathis, 2022. "Extracting Proceedings Data from Court Cases with Machine Learning," Stats, MDPI, vol. 5(4), pages 1-16, December.
    11. Bălan Carmen, 2018. "The Impact of Conversational Agents on Humans in Services: Research Questions and Hypotheses," International Conference on Marketing and Business Development Journal, The Bucharest University of Economic Studies, vol. 1(2), pages 33-55, December.
    12. , Aisdl, 2020. "Becoming Attuned," OSF Preprints j7f8y, Center for Open Science.
    13. Bokwon Lee & Kyu-Min Lee & Jae-Suk Yang, 2019. "Network structure reveals patterns of legal complexity in human society: The case of the Constitutional legal network," PLOS ONE, Public Library of Science, vol. 14(1), pages 1-15, January.
    14. Giansiracusa, Noah & Ricciardi, Cameron, 2019. "Computational geometry and the U.S. Supreme Court," Mathematical Social Sciences, Elsevier, vol. 98(C), pages 1-9.
    15. Daniyal Alghazzawi & Omaimah Bamasag & Aiiad Albeshri & Iqra Sana & Hayat Ullah & Muhammad Zubair Asghar, 2022. "Efficient Prediction of Court Judgments Using an LSTM+CNN Neural Network Model with an Optimal Feature Set," Mathematics, MDPI, vol. 10(5), pages 1-30, February.
    16. Yang, Guancan & Lu, Guoxuan & Xu, Shuo & Chen, Liang & Wen, Yuxin, 2023. "Which type of dynamic indicators should be preferred to predict patent commercial potential?," Technological Forecasting and Social Change, Elsevier, vol. 193(C).
    17. Diller, Markus & Asen, Martin & Späth, Thomas, 2020. "The effects of personality traits on digital transformation: Evidence from German tax consulting," International Journal of Accounting Information Systems, Elsevier, vol. 37(C).
    18. Mindock, Maxwell R. & Waddell, Glen R., 2019. "Vote Influence in Group Decision-Making: The Changing Role of Justices' Peers on the Supreme Court," IZA Discussion Papers 12317, Institute of Labor Economics (IZA).
    19. Zhong, Weifeng & Chan, Julian & Ho, Kwan-Yuet & Lee, Kit, 2020. "Words Speak Louder Than Numbers: Estimating China’s COVID Severity with Deep Learning," Working Papers 10955, George Mason University, Mercatus Center.
    20. Qian Hongdao & Sughra Bibi & Asif Khan & Lorenzo Ardito & Mohammad Nurunnabi, 2019. "Does What Goes Around Really Comes Around? The Mediating Effect of CSR on the Relationship between Transformational Leadership and Employee’s Job Performance in Law Firms," Sustainability, MDPI, vol. 11(12), pages 1-19, June.

    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:gam:jsusta:v:13:y:2021:i:14:p:8085-:d:597574. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.