Cultural heritage institutions, such as museums and galleries, today face the challenge of managing an increasing volume of unsolicited visitor feedback generated across online platforms. This study offers a practical and scalable methodology that transforms 5856 multilingual Google reviews from 59 globally ranked museums and galleries into actionable insights through sentiment analysis, correlation diagnostics, and guided Latent Dirichlet Allocation. By addressing the limitations of prior research, such as outdated datasets, monolingual bias, and narrow geographical focus, the authors analyze a current and diverse set of recent reviews to capture a timely and globally relevant perspective on visitor experiences. The adopted guided LDA model identifies 12 key topics, reflecting both operational issues and emotional responses. The results indicate that while visitors generally express overwhelmingly positive sentiments, dissatisfaction tends to be concentrated in specific service areas. Correlation analysis reveals that longer, emotionally rich reviews are more likely to convey stronger sentiment and receive peer endorsement, highlighting their diagnostic significance. From a practical perspective, the methodology empowers professionals to prioritize improvements based on data-driven insights. By integrating quantitative metrics with qualitative topics, this study supports operational decision-making and cultivates a more empathetic and responsive data management mindset for museums. The reproducible and adaptable nature of the pipeline makes it suitable for cultural institutions of various sizes and resources. Ultimately, this work contributes to the field of cultural informatics by bridging computational precision with humanistic inquiry. That is, it illustrates how intelligent analysis of visitor reviews can lead to a more personalized, inclusive, and strategic museum experience.

The pandemic and the various natural disasters related to climate change force Universities to operate remotely during certain periods of time. Synchronous and asynchronous Learning Management System (LMS) platforms at University provide a framework for distant operation. But it is crucial to maintain low students' attrition rates and ensure the quality of offered education at universities. This paper presents a survey of students' perceptions regarding their participation in online distance examinations in a Department of a Greek University. In this research, an online questionnaire is used in order to capture students' opinions about online distance examinations and to reveal information concern accessibility issues during the educational activities. The survey also recorded the students' opinion concerning the use of distance learning platforms, the communication with the professors and interactions between the students. According to the analysis of the questionnaires, students are willing to express their opinion on critical issues, such as the distance examinations and the problems that they face this period anonymously. To a satisfactory degree, they have the necessary equipment needed to participate in the distance educational process and they use the existing blended learning infrastructure.

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.