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

Israel Griol-Barres, Sergio Milla, Antonio Cebrián, Huaan Fan and Jose Millet
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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

Sustainability, 2020, vol. 12, issue 19, 1-22

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.

Keywords: new sustainable business models; business intelligence; natural language processing; weak signals of the future; predictive models; text mining (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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