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Detecting Weak Signals of the Future: A System Implementation Based on Text Mining and Natural Language Processing

Author

Listed:
  • Israel Griol-Barres

    (IDEAS-UPV, Vice-Rectorate for Entrepreneurship and Employment, Polytechnic University of Valencia, 46022 Valencia, Spain)

  • Sergio Milla

    (FGYM, Vice-Rectorate for Entrepreneurship and Employment, Polytechnic University of Valencia, 46022 Valencia, Spain)

  • Antonio Cebrián

    (Instituto ITACA, Polytechnic University of Valencia, 46022 Valencia, Spain)

  • Huaan Fan

    (Division of Geodesy and Satellite Positioning, Royal Institute of Technology (KTH), SE-100 44 Stockholm, Sweden)

  • Jose Millet

    (Instituto ITACA, Polytechnic University of Valencia, 46022 Valencia, Spain)

Abstract

Organizations, companies and start-ups need to cope with constant changes on the market which are difficult to predict. Therefore, the development of new systems to detect significant future changes is vital to make correct decisions in an organization and to discover new opportunities. A system based on business intelligence techniques is proposed to detect weak signals, that are related to future transcendental changes. While most known solutions are based on the use of structured data, the proposed system quantitatively detects these signals using heterogeneous and unstructured information from scientific, journalistic and social sources, applying text mining to analyze the documents and natural language processing to extract accurate results. The main contributions are that the system has been designed for any field, using different input datasets of documents, and with an automatic classification of categories for the detected keywords. In this research paper, results from the future of remote sensors are presented. Remote sensing services are providing new applications in observation and analysis of information remotely. This market is projected to witness a significant growth due to the increasing demand for services in commercial and defense industries. The system has obtained promising results, evaluated with two different methodologies, to help experts in the decision-making process and to discover new trends and opportunities.

Suggested Citation

  • Israel Griol-Barres & Sergio Milla & Antonio Cebrián & Huaan Fan & Jose Millet, 2020. "Detecting Weak Signals of the Future: A System Implementation Based on Text Mining and Natural Language Processing," Sustainability, MDPI, vol. 12(19), pages 1-22, September.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:19:p:7848-:d:417789
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    References listed on IDEAS

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    Cited by:

    1. Hyundong Nam & Taewoo Nam, 2021. "Exploring Strategic Directions of Pandemic Crisis Management: A Text Analysis of World Economic Forum COVID-19 Reports," Sustainability, MDPI, vol. 13(8), pages 1-19, April.
    2. Muhammad Salman Pathan & Edana Richardson & Edgar Galvan & Peter Mooney, 2023. "The Role of Artificial Intelligence within Circular Economy Activities—A View from Ireland," Sustainability, MDPI, vol. 15(12), pages 1-18, June.
    3. Ebadi, Ashkan & Auger, Alain & Gauthier, Yvan, 2022. "Detecting emerging technologies and their evolution using deep learning and weak signal analysis," Journal of Informetrics, Elsevier, vol. 16(4).
    4. U. M. Fernandes Dimlo & V. Rupesh & Yeligeti Raju, 2024. "The dynamics of natural language processing and text mining under emerging artificial intelligence techniques," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 15(9), pages 4512-4526, September.
    5. Nhu Khoa Nguyen & Thierry Delahaut & Emanuela Boros & Antoine Doucet & Gael Lejeune, 2023. "Contextualizing Emerging Trends in Financial News Articles," Papers 2301.11318, arXiv.org.
    6. Mauno, Tuomas & Catelo, Fellice & Bengston, David N. & Pykäläinen, Jouni & Hujala, Teppo, 2023. "How to identify and interpret weak signals of change in the forest bioeconomy," Forest Policy and Economics, Elsevier, vol. 157(C).

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