| Title |
Detecting Weak Signals of the Future: A System Implementation Based on Text Mining and Natural Language Processing |
| ID_Doc |
73627 |
| Authors |
Griol-Barres, I; Milla, S; Cebrián, A; Fan, HA; Millet, J |
| Title |
Detecting Weak Signals of the Future: A System Implementation Based on Text Mining and Natural Language Processing |
| Year |
2020 |
| Published |
Sustainability, 12, 19 |
| DOI |
10.3390/su12197848 |
| 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. |
| Author Keywords |
new sustainable business models; business intelligence; natural language processing; weak signals of the future; predictive models; text mining |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) |
| EID |
WOS:000586637800001 |
| WoS Category |
Green & Sustainable Science & Technology; Environmental Sciences; Environmental Studies |
| Research Area |
Science & Technology - Other Topics; Environmental Sciences & Ecology |
| PDF |
https://www.mdpi.com/2071-1050/12/19/7848/pdf?version=1600861394
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