Unlocking User-Generated Content for Academic Library Service Quality Assessment: A Multidimensional Text Analytics Framework

While traditional metrics and survey-based models such as LibQUAL+ grant valuable foundations for evaluating academic library service quality, they may not fully capture the immediacy and nuance of evolving patron experiences. As libraries increasingly become interactive community hubs, User-Generated Content, including Google reviews, provides a rich, authentic source of unfiltered feedback. Recognizing both the scalability obstacles of manual coding and the semantic limitations of conventional probabilistic models, this study puts forward a multidimensional text analytics framework designed to bridge these gaps with precision and sensitivity to user voices. Examining 8542 multilingual Google reviews from 88 academic libraries across a broad, multinational sample, this approach adopts a Zero-Shot Classification pipeline for theoretically anchored thematic categories with a rating-fortified VADER sentiment analysis to capture delicate emotional dimensions. Findings highlight a clear distribution of thematic feedback. While physical spaces and community events generate the highest satisfaction, digital infrastructure and staff interactions are identified as primary operational challenges. Finally, this data-driven framework seeks to transform unstructured UGC into actionable intelligence, supporting library administrators in making dynamic, evidence-based decisions that respond to the genuine needs and lived experiences of their communities.