Sentiment Analysis of Roblox Genres on TikTok: Roleplay, RPG, and Horror using Naïve Bayes

Authors

  • Keisha Jovie Dermawan Fakultas Sains dan Teknologi, Universitas Pradita
  • Nur Amalina Sabrina Fakultas Sains dan Teknologi, Universitas Pradita
  • Greycia Febrina Michelle Fakultas Sains dan Teknologi, Universitas Pradita
  • Afifah Trista Ayunda Fakultas Sains dan Teknologi, Universitas Pradita https://orcid.org/0009-0003-6069-4147

DOI:

https://doi.org/10.51903/pkbfwq48

Keywords:

Sentiment Analysis, Roblox, TikTok, Naïve Bayes, Text Classification

Abstract

The popularity of the Roblox game platform in Indonesia is reflected in the high volume of user interactions on TikTok. However, this massive comment data is unstructured and heavily uses slang, making it difficult to identify specific player preferences. This study aims to analyze user sentiment and compare preferences across three main game categories in the Roblox ecosystem: Roleplay, RPG, and Horror. The research applies a Text Mining approach using the Multinomial Naïve Bayes algorithm to classify comments collected from TikTok. The results show that the model performs well, achieving a global accuracy of 77.78% and precise negative sentiment detection. The findings reveal that the global sentiment distribution is dominated by Neutral and Negative responses. Comparative analysis reveals contrasting patterns: the Horror genre recorded the highest negative sentiment ratio, driven by unpleasant physical experiences, while the RPG genre recorded the highest positive sentiment, driven by satisfaction with in-game achievements. In conclusion, technical stability and gameplay comfort are the main determinants of player preferences.

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Published

2026-01-21

Issue

Vol. 5 No. 1 (2026): January: Jurnal Ilmiah Sistem Informasi

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Articles

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Copyright (c) 2026 Keisha Jovie Dermawan, Nur Amalina Sabrina, Greycia Febrina Michelle, Afifah Trista Ayunda

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