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All Journal Journal Of Artificial Intelligence And Software Engineering
Pebriana, Sela
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Computer Science & IT

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Sentiment Analysis of Netizen Opinions on TikTok Towards iPhone Using Naïve Bayes Algorithm and Support Vector Machine (SVM) Pebriana, Sela; Sugianto, Castaka Agus
Journal of Artificial Intelligence and Software Engineering Vol 5, No 3 (2025): September
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i3.7011

Abstract

This study aims to analyze TikTok users’ sentiment toward the iPhone by utilizing TikTok comments as the primary data source. TikTok was chosen due to its high user engagement and ease of access to spontaneous public opinions. A total of 964 comments were collected and processed through a data cleaning stage. The sentiments were classified into positive and negative categories using two popular machine learning algorithms: Naïve Bayes and Support Vector Machine (SVM). This comparison was conducted to evaluate the effectiveness of each algorithm in handling local social media data, which is typically brief and unstructured. The results show that Naïve Bayes achieved an accuracy of 74%, while SVM reached 71%. These findings indicate that Naïve Bayes performs better in fast sentiment analysis of short-text public opinions and has practical potential for monitoring consumer perception and supporting efficient digital marketing strategies.
Co-Authors Sugianto, Castaka Agus