Bounab, Y., Oussalah, M. A new knowledge discovery approach for mining business trade barriers. J Intell Inf Syst 59, 567–590 (2022). https://doi.org/10.1007/s10844-022-00701-z
A new knowledge discovery approach for mining business trade barriers
|Author:||Bounab, Yazid1; Oussalah, Mourad1|
1Faculty of ITEE, CMVS, University of Oulu, Pentti Kaiteran katu 1, 90570, Oulu, 90014, Finland
|Online Access:||PDF Full Text (PDF, 2 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2023033134210
|Publish Date:|| 2023-03-31
Cross-border trade barriers introduced by national authorities to protect local business and labor force cause substantial damage to international economical actors. Therefore, identifying such barriers beyond regulator’s audit reporting is of paramount importance. This paper contributes towards this goal by proposing a novel approach that uses natural language processing and deep learning method for uncovering Finnish-Russian trade barriers in the fish industry from selected business discussion forums. Especially, the approach makes use i) a three-leg ontology for data collection, ii) a BERT architecture for mapping Onkivisit-Shaw-Kananen trade barrier ontology to negative polarity posts and, iii) a new reverse-engineering clustering approach to identify the causes of individual trade-barrier types. A comparison with official statistical reports has been carried out to identify the salient aspects of trade-barriers that hold regardless of the time difference. The findings reveal the dominance of the Time-length barrier type in the Finnish discussion forum dataset and import vs export tariff discrepancy and product requirement barrier types in the Russian forum dataset. The developed framework can serve as a tool to assist companies or regulators in providing business-related recommendations to overcome the detected trade barriers.
Journal of intelligent information systems
|Pages:||567 - 590|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
113 Computer and information sciences
Open Access funding provided by University of Oulu including Oulu University Hospital. This work is partly supported by the EU CBC Karelia on IoT and Business Creation, as well as the H2020 YougRes project (Ref. 823701), which are gratefully acknowledged.
All data generated by this work are available in the supplementary file of this submission and Github project provided in this paper.
© 2022, The Author(s). This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.