Identifying social concerns in virtual reality technology through text mining and large language models, and prioritizing them with the fuzzy hierarchized analytic network process

Virtual reality technology has rapidly gained popularity as an entertainment medium, drawing interest from diverse age groups. However, its widespread adoption depends on effectively addressing public concerns and achieving market acceptance. While some studies have acknowledged these concerns, a significant gap persists in comprehensive research that incorporates both individual and expert perspectives. Consequently, certain underlying social issues related to virtual reality systems remain unexplored and unprioritized. To address this gap, this paper proposes a methodology that utilizes Latent Semantic Analysis (LSA) to identify and assess social concerns from various sources, including user perspectives. Large Language Models (LLMs) assist in retrieving relevant chunks of articles during analysis, enhancing data quality. Furthermore, we introduce a novel decision-making tool, the Hierarchized Analytic Network Process (HANP) and its fuzzy form, to effectively rank these concerns. This approach addresses a limitation of the traditional Analytic Network Process (ANP), which can overemphasize dependent attributes, potentially leading to zero-weighted, less important attributes and making comparisons impossible. By prioritizing social concerns based on their significance, our approach aims to facilitate broader social acceptance of virtual reality technologies among the general public. To further demonstrate the advantages of our proposed approach, the results obtained from F-HANP (in situations where fuzzy judgments are available) and HANP are compared with other popular decision-making methods.