A risky large group emergency decision-making method based on topic sentiment analysis

This study proposes a decision-making method based on topic sentiment analysis to address the problem of completely data-driven attribute information acquisition and risk control of the intuitionistic fuzzy preference in large group emergency decision-making. First, Latent Dirichlet Allocation (LDA) topic mining is applied to rank public topics and construct an emergency sentiment dictionary for topic sentiment analysis. The attribute system structure and weight information of large group emergency decision-making can be obtained by transforming the high-concern topic sentiment value. Second, with the public attention attribute and public attribute preference as references for large group emergency decision-making, risk measurement under the intuitionistic fuzzy preference model is based on risk credibility. The risk-consensus feedback mechanism of large group emergency decision-making is designed to obtain the high-consensus and low-risk alternative. Finally, the applicability and effectiveness of the method are demonstrated using a case study involving a serious explosion accident.

Automated summary

This study proposes a decision-making method based on topic sentiment analysis to address the problem of completely data-driven attribute information acquisition and risk control in large group emergency decision-making. The method uses topic mining and sentiment analysis to obtain attribute system structure and weight information, and measures risk using risk credibility. A risk-consensus feedback mechanism is also designed to obtain high-consensus and low-risk alternatives.