An incomplete three-way consensus algorithm for unmanned aerial vehicle purchase using optimization-driven sentiment analysis

Abstract

As a novel productive force in the low-altitude economy, the unmanned aerial vehicle (UAV) industry has emerged as a crucial engine of the digital economy growth. However, high-dimensional online reviews, incomplete information systems (IISs), and coordination among numerous sellers may influence the purchasing decision for UAVs. To address these challenges, first, the sentiment analysis (SA) of UAV online reviews is conducted using BiLSTM and BiGRU models optimized by the hippopotamus optimization (HO) algorithm. Meanwhile, the K-nearest neighbor (KNN) algorithm that combines the Jensen–shannon (JS) divergence with the Hellinger distance is applied to construct a complete information system (CIS). Second, three-way clustering (TWC) is performed on sellers, followed by the calculation of seller weights and group weights using the full consistency method. Third, to closely align with the behavior of sellers, a two-stage consensus reaching process (CRP) model based on TWC and the dual fine-tuning (DFT) theory is proposed, referred to as TWC-DFT-CCRP. In the first stage, the behavior of sellers is adjusted based on the TWC result. In the second stage, optimization-based rules are used to reduce the conflict degree among sellers to reach consensus. Fourth, integrating the TWD process with prospect regret theory (P-RT) can reduce potential decision risks and identify the optimal solution. Finally, the model’s feasibility is demonstrated via a case study of UAV online reviews. In summary, the method not only addresses the challenge of handling high-dimensional data but also optimizes large-scale group decision-making (LSGDM), thereby providing effective decision support for purchasing UAVs.