<![CDATA[In the era of digital feedback, higher education institutions face growing challenges in making sense of large volumes of user-generated reviews, especially those written in multiple languages. This study analyzes 1,717 Google Maps reviews related to Universitas Muhammadiyah Surakarta (UMS), collected over five years in Bahasa Indonesia and English. To overcome limitations of manual and monolingual sentiment analysis, we employed a pre-trained multilingual transformer model—lxyuan/distilbert-base-multilingual-cased-sentiments-student—without additional fine-tuning. The analysis revealed that 88% of reviews were classified as positive, with most praise directed at campus facilities, while criticism often targeted administrative services. Beyond sentiment classification, this study explored text length, confidence scores, and user engagement patterns to uncover deeper behavioral insights. We also developed SentiMu, an interactive dashboard that visualizes sentiment trends, recent reviews, word clouds, and key metrics, enabling university stakeholders to monitor feedback in real time. The dashboard was built using Next.js and FastAPI for optimal performance and scalability. By automating the analysis and visualization of multilingual online reviews, this study provides a practical and scalable framework for institutions to understand student and visitor experiences, supporting data-driven decisions to enhance campus services and reputation.]]>
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