<![CDATA[Student satisfaction with university facilities and services requires in-depth analysis to ensure improvements in unsatisfactory facilities or services while maintaining those that meet expectations. This study aims to analyze sentiment in student satisfaction surveys using Natural Language Processing (NLP) methods. Survey data collected from 2022 to 2024 were analyzed using two main approaches: Naive Bayes (NB) with n-grams (n=1,2,3) employing feature extraction methods such as Term Frequency-Inverse Document Frequency (TF-IDF) and Bag of Words (BoW), and Bidirectional Encoder Representations from Transformers (BERT). The analysis results indicate that BERT outperforms NB in terms of sentiment prediction accuracy, although the difference is not highly significant. This study also identified keywords for both positive and negative sentiments. These keywords were then analyzed across 11 categories of facilities and services to provide focused insights into aspects that need to be maintained or improved. This study concludes that sentiment analysis provides significant contributions to universities in evaluating and enhancing the quality of facilities and services according to student preferences.]]>
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