Sentiment Analysis of Tantan Application Users: A Performance Comparison Between Naive Bayes and SVM
Analisis Sentimen Pengguna Aplikasi Tantan: Perbandingan Kinerja Metode Naive Bayes dan SVM
DOI:
https://doi.org/10.21070/ups.6978Keywords:
Sentiment Analysis, Tantan, Naive Bayes, Support Vector Machine, Sigmoid KernelAbstract
Tantan, as a popular dating application in Indonesia, has garnered various user reviews reflecting their experiences. This study analyzes user sentiment for the Tantan application by comparing the performance of Naive Bayes and Support Vector Machine (SVM) algorithms in sentiment classification. User reviews were collected from Google Play Store using web scraping techniques and processed through data cleaning, tokenization, and TF-IDF feature extraction. The dataset comprises 1,195 reviews, with 74.6% positive and 25.4% negative sentiments. The Naive Bayes model achieved an accuracy of 85.36%, excelling in detecting positive reviews (precision 86%, recall 97%) but performing suboptimally on negative reviews (recall 44%). Conversely, the SVM model with a sigmoid kernel demonstrated superior performance, achieving an accuracy of 87.03%. It handled negative reviews better, with a recall of 67% and an F1-score of 69%, while maintaining excellent results for positive reviews (precision 91%, F1-score 92%). SVM is recommended for its balanced performance.
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