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All Journal EDUMATIC: Jurnal Pendidikan Informatika
Pratama, Rio Ferdinand Putra
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Computer Science & IT Education

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Comparative Analysis of Naive Bayes and SVM for Improved Emotion Classification on Social Media Pratama, Rio Ferdinand Putra; Maharani, Warih
Jurnal Pendidikan Informatika (EDUMATIC) Vol 9 No 1 (2025): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v9i1.29087

Abstract

identifying emotions such as happy, angry, sad, and fear. However, Indonesian text processing faces challenges due to language complexity and slang. This research aims to compare Naive Bayes and SVM models, focusing on evaluating the impact of preprocessing, feature extraction, and parameter optimization to improve emotion classification. The dataset was collected from API X using crawling techniques and manually annotated by six annotators. The training process used full and half preprocessing datasets with TF-IDF, BoW, and Word2Vec feature extraction. Naive Bayes and SVM models were evaluated using accuracy, precision, recall, and F1 score. Our results show that full preprocessing improves accuracy, with TF-IDF + BoW achieving 78.01% with SVM and outperforming Naïve Bayes at 75.53%. The results classify emotions into four classes: happy, sad, angry, and fear. This study demonstrates the value of preprocessing and feature selection to deal with slang and complexity in Indonesian texts. These results provide insights for developing optimal emotion classification models and offer applications in sentiment analysis, social media monitoring, and mental health detection.
Co-Authors Warih Maharani