<![CDATA[The rapid development of digital learning applications has significantly expanded public access to technology-based educational services. As the number of users continues to grow, user reviews published on application distribution platforms have become an important source of information for evaluating service quality and user satisfaction. Sentiment analysis has emerged as one of the most widely adopted approaches for automatically identifying user opinions through text mining and machine learning techniques. This study aims to review the application of the Term Frequency–Inverse Document Frequency (TF-IDF) method and the Support Vector Machine (SVM) algorithm for sentiment analysis of user reviews on educational applications based on previous studies. The research employed a literature review approach by examining scientific articles published between 2020 and 2025 and indexed in Google Scholar, IEEE Xplore, ScienceDirect, SpringerLink, and Scopus. The analysis focused on comparing the methods, text preprocessing techniques, feature extraction approaches, and classification performance reported in previous studies. The review indicates that the combination of TF-IDF and SVM is among the most widely used approaches for sentiment analysis due to its effective feature representation and stable classification performance in text-based datasets. Furthermore, the effectiveness of the classification process is influenced by text preprocessing, dataset characteristics, and model parameter selection. This study is expected to serve as a valuable reference for researchers and application developers in selecting appropriate sentiment analysis methods for digital learning applications.]]>
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