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Evaluation of Naive Bayes and SVM Performance in Predicting the Sentiment of User Reviews on the Pinterest Application

Evaluasi Performa Naive Bayes dan SVM dalam Memprediksi Sentimen Ulasan Pengguna Aplikasi Pinterest

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DOI:

https://doi.org/10.21070/ups.10598

Keywords:

Sentiment Analysis, Pinterest, Naive Bayes, Support Vector Machine, Random Oversampling

Abstract

Advances in digital technology have driven the increased use of mobile applications, including Pinterest, which generates many user reviews as a source of satisfaction information. However, unstructured text reviews and imbalanced sentiment data make manual analysis difficult and potentially biased. This study compares the performance of Naive Bayes and Support Vector Machine in classifying the sentiment of Pinterest user reviews. The research methods include pre-processing, labelling, feature extraction using Term Frequency Inverse Document Frequency, data splitting at ratios of 80:20 and 70:30, and handling unbalanced data using Random Oversampling. Evaluation was conducted using a confusion matrix with accuracy, precision, recall, and F1-score metrics. The results show that Support Vector Machine under the 80:20 imbalanced condition achieved the highest accuracy of 89.01 percent and F1-score of 88.71 percent, while Naive Bayes with a 70:30 Random Oversampling ratio produced the highest negative class recall of 87.03 percent.

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2026-04-08

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