A Large Group Emergency Decision-Making Approach on HFLTS With Public Preference Data Mining

Abstract

Aiming at the emergency decision-making problem of major emergencies, this article proposes a large group emergency decision-making (LGEDM) approach with public opinions mining on hesitation fuzzy language term set (HFLTS). First, extract keywords that represent general preferences on events from the Weibo platform, classify keywords using the word similarity-based keyword clustering algorithm and identify decision attributes and their weights. Next, define the similarity measure and hesitation fuzzy entropy measure of HFLTS, quantify the decision risk of experts using the risk measurement model, and cluster all experts into several subgroups using the risk metric-based group clustering algorithm. Subsequently, assign clusters' weights on their risk value and size and obtain each cluster's preference matrix by the HIOWA operator. Finally, derive the ranking results of alternatives using the sorting process, and an example of “COVID-19” is presented to verify the rationality and effectiveness of the proposed method.