Application of Machine Learning Algorithm to Prediction User Satisfaction Sentiment on Weverse
Penerapan Algoritma Machine Learning untuk Memprediksi Sentimen Kepuasan Pengguna Weverse
DOI:
https://doi.org/10.21070/ups.9745Keywords:
Sentiment Analysis, Decision Tree, Naïve Bayes, Random Oversampling (ROS), Weverse ReviewAbstract
The rapid development of digital technology has increased the use of fan community platforms such as Weverse, generating user reviews as indicators of application service satisfaction. However, Google Play Store reviews are unstructured and have imbalanced sentiment class distributions, making sentiment classification challenging. This study aims to compare the performance of Decision Tree and Naïve Bayes algorithms in classifying Weverse user review sentiment. The methodology includes text preprocessing, semi-automatic labeling, feature extraction using Term Frequency–Inverse Document Frequency (TF-IDF), and handling class imbalance using Random Oversampling (ROS) on training data. Model evaluation was conducted using a Confusion Matrix with data split ratios of 80:20 and 70:30. The results show that the Naïve Bayes algorithm with an 80:20 split achieved the best performance, with an
accuracy of 86.81% and an F1-score of 83.88%. The application of ROS improved sentiment classification balance.
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