A Weighted Information Fusion Method Based on Sentiment Knowledge for Emergency Decision-Making Considering the Public and Experts

Xuanhua Xu, Kaixia Zheng () and Bin Pan ()
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Xuanhua Xu: Central South University
Kaixia Zheng: Central South University
Bin Pan: Hunan University of Finance and Economics

Group Decision and Negotiation, 2024, vol. 33, issue 2, No 6, 398 pages

Abstract: Abstract The information released by the public on various social media platforms can assist experts in decision-making, thus improving the reliability of the decision-making results and increasing their likelihood of gaining public support. However, the determination of how to fully integrate public opinions with expert opinions and in what aspects to realize the integration is a research topic worthy of attention. Based on this, this paper proposes a multi-subject weighted information fusion method that fully considers public opinion. First, the Bayesian subject model is used to obtain the attribute set of public opinion, and the sentiment analysis technique is used to calculate the reference attribute weights. Second, public opinion is utilized to improve the traditional best–worst method to determine the subjective attribute weights of experts at a faster rate. Third, expert weights are determined using “knowledge” and attribute weights are defined at the aggregate level. Fourth, the cluster weights are determined according to the proximity of the clustered opinions to the public opinions, and the clustered opinions are then summarized to get the overall decision-making information, thus scientifically realizing the synergistic decision-making between the public and experts. Finally, the case of a rainstorm emergency in Henan Province, China, is used to analyze and determine the best plan that matches with the actual situation, and the results are then compared with the findings of existing research to illustrate the feasibility and effectiveness of the proposed method.

Keywords: Sentiment knowledge; Multi-agent; Emergency decision-making; Public behavior data; Sentiment analysis (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10726-023-09865-7

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