Objective and Intelligent Acquisition of Two Types of Decision Data Based on Online Comment Information and Its Application in Emergency Decision-Making
Abstract Given the urgent, dynamic, and uncertain nature of emergencies, the rapid response to emergencies and their control is challenging, with the intelligent, real-time acquisition of decision data being a key issue. However, most previous studies relied on simulations, experts, assumptions, que...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-08-01
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| Series: | International Journal of Computational Intelligence Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s44196-025-00953-x |
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| Summary: | Abstract Given the urgent, dynamic, and uncertain nature of emergencies, the rapid response to emergencies and their control is challenging, with the intelligent, real-time acquisition of decision data being a key issue. However, most previous studies relied on simulations, experts, assumptions, questionnaires, and borrowed literature, which are impractical. Therefore, this study developed two methods for the intelligent and objective acquisition of quantitative decision data based on machine learning and online review data and applied the methods to solve practical emergency problems. First, a reasonable decision index system was constructed using latent Dirichlet allocation thematic-cluster analysis of the focus word frequency from online public opinions. Python programming was then used to capture the big data of social media comments on emergencies in real time to obtain multisource comment data, which were preprocessed and visualized. Second, based on the optimized SnowNLP, emotional tendencies and statistical analyses were performed on the processed comment data, resulting in two quantitative decision matrices, which were represented as single-valued neutrosophic numbers (SVNNs) and probabilistic linguistic terms (PLTs). Next, the bidirectional projection method was extended to an environment of SVNNs and PLTs, and the alternatives were sorted and selected. Finally, a sudden natural disaster event was used as a numerical case to verify the proposed method. Through a comparative analysis, the experimental results show the practicability and feasibility of the proposed decision-making method, demonstrating the superiority and intelligence of our research. Our study can conduct real-time monitoring of emergencies and intelligently and objectively obtain quantified decision-making data, thereby providing auxiliary decision-making support for relevant emergency management departments, ensuring the positive development of public opinion, social stability, and the safety of people's lives and property. |
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| ISSN: | 1875-6883 |