Utilizing unstructured data to predict the art museum visitor numbers using deep learning approaches

This study proposes a modified and adaptive Balanced Scorecard (BSC) framework tailored to the strategic context of art museums as its primary contribution. The adapted BSC categorizes museum strategies into four dimensions-Social Values and Cultural Impact (SVCI), Visitor Experience and Tourism Development (VED), Exhibition and Operational Efficiency (EOE), and Art/Cultural Education and Sustainable Growth (ACES)-providing a systematic lens for interpreting and quantifying qualitative managerial priorities. These strategic perspectives were extracted from unstructured textual data sourced from museum websites and visitor-generated online content through natural language processing and text mining techniques. Building on this strategic classification, the study develops deep learning-based predictive models that integrate both structured numerical data and text-derived qualitative variables to estimate visitor numbers. Eight deep learning architectures, including Transformer, Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and Generative Adversarial Network (GAN), were evaluated. The results indicate that VED exerts the most substantial influence on visitor attendance, particularly through interactive exhibitions, personalized experiences, and tourism collaborations, while other strategic dimensions provide marginal improvements in predictive accuracy. The Transformer model achieved the best predictive performance, reflecting its superior ability to capture complex relationships between structured and unstructured data. By combining an adapted BSC framework with advanced predictive modeling, this study offers both a novel theoretical approach for categorizing museum strategies and a practical tool for enhancing decision-making in visitor engagement, exhibition planning, and resource allocation.