The user experience of university library: A text mining analysis of a Q&A platform in China

Abstract

To gain a deeper understanding of the user experience in university libraries, an alternative channel for collecting user feedback was explored utilizing a prominent Chinese Question and Answer (Q&A) platform, Zhihu. A dataset consisting of 11 questions and 12,647 valid answers related to the user experience of university libraries on the Zhihu platform was collected. To analyze the collected user comments (answers) quantitatively, various techniques including word frequency analysis, semantic network analysis, Latent Dirichlet Allocation (LDA) topic modeling, and sentiment analysis were used. Findings revealed that factors influencing user experience can be categorized into six main groups: university life and future planning, choice and efficiency of study spaces, library resource management and staff behavior, seat usage behavior, noise issues, and the behavior of other users. Sentiment analysis revealed a mix of emotions in user experience. Positive experiences stemmed from quality learning environments and personal development support, while negative experiences were primarily caused by noise, seat scarcity, management issues, and other users' behavior. These varied emotional responses and suggested targeted improvements were explored. Findings could contribute to a deeper understanding of the user experience in university libraries and offer practical insights for improving library services.