<![CDATA[The rapid growth of mobile applications has generated large volumes of user reviews, making automated sentiment analysis essential for understanding user perceptions. Previous studies have shown that while machine learning models perform well in binary sentiment classification, they often struggle in multi-class settings, particularly in identifying neutral sentiment due to linguistic ambiguity and class imbalance. This study aims to comparatively evaluate the performance of Support Vector Machine (SVM) and Random Forest in multi-class sentiment analysis, with a specific focus on their ability to handle the neutral sentiment category. A supervised learning approach was employed using 2,112 Indonesian-language user reviews collected from the Google Play Store. The data were preprocessed using standard Natural Language Processing techniques and represented using TF-IDF features. Both models were trained and evaluated using accuracy, precision, recall, F1-score, and confusion matrices. The results indicate that SVM achieved an accuracy of 86.52%, outperforming Random Forest, which obtained 83.45%. However, both models completely failed to classify the neutral sentiment class, yielding zero precision and recall for this category. This failure highlights the dominant influence of severe class imbalance and insufficient feature discrimination for neutral sentiment. The findings underscore a critical limitation of traditional machine learning approaches in multi-class sentiment analysis and emphasize the need for improved strategies, such as data resampling, advanced feature representation, or hybrid models, to enhance neutral sentiment detection in real-world applications.]]>
