Understanding consumer perception and acceptance of AI art through eye tracking and Bidirectional Encoder Representations from Transformers-based sentiment analysis.

Abstract

This study investigates public perception and acceptance of AI-generated art using an integrated system that merges eye-tracking methodologies with advanced bidirectional encoder representations from transformers (BERT)-based sentiment analysis. Eye-tracking methods systematically document the visual trajectories and fixation spots of consumers viewing AI-generated artworks, elucidating the inherent relationship between visual activity and perception. Thereafter, the BERT-based sentiment analysis algorithm extracts emotional responses and aesthetic assessments from numerous internet reviews, offering a robust instrument for evaluating public approval and aesthetic perception. The findings indicate that consumer perception of AI-generated art is markedly affected by visual attention behavior, whereas sentiment analysis uncovers substantial disparities in aesthetic assessments. This paper introduces enhancements to the BERT model via domain-specific pre-training and hyperparameter optimization utilizing deep Gaussian processes and dynamic Bayesian optimization, resulting in substantial increases in classification accuracy and resilience. This study thoroughly examines the underlying mechanisms of public perception and assessment of AI-generated art, assesses the potential of these techniques for practical application in art creation and evaluation, and offers a novel perspective and scientific foundation for future research and application of AI art.