From Data to Insights: Perceiving Museum Experiences Through Natural Language Processing of Social Media Corpus

In the context of the experience economy era, museums have evolved from static exhibition spaces to multidimensional cultural experience hubs. Social media plays a crucial role in evaluating public satisfaction. This study aims to examine public perceptions of museum experiences, focusing on thematic content and overall emotional trends toward museums. While natural language processing (NLP) models have been widely applied in the field of computing, their use in museum experience design, particularly in the Chinese context, remains limited. This study employs the pre-trained model in NLP for BERTopic modeling, this research analyzes 14,800 recent online reviews (dated January 1, 2024 - December 31, 2024) from China's top 10 most-searched museums on Dianping. Generating three core topics: technological applications, cultural immersion, and service quality. Within these themes, several conflict issues emerge, such as difficulties in ticket reservation systems, the fragmentation of experiences due to digital technology, and the tension between artifact display technologies and the commercialization of cultural products. Sentiment analysis using SnowNLP indicates that while architectural design, exhibition content, and technological applications generally elicit positive emotions, satisfaction with ticketing systems and service efficiency remains low. Additionally, immersive experiences show high levels of visitor engagement, but concerns arise regarding the excessive complexity of technology, which affects the balance between technology use and historical-cultural appreciation. These findings provide valuable insights for improving museum experience design, particularly in enhancing service quality, balancing technological applications, and preserving cultural heritage.