Comparative Analysis of Naïve Bayes Classifier and Support Vector Machine for Multilingual Sentiment Analysis: Insights from Genshin Impact User Reviews

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JUSIFO (Jurnal Sistem Informasi) | Vol. 10, No. 2, Desember 2024
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Published: Dec 31, 2024
DOI: https://doi.org/10.19109/jusifo.v10i2.24876
Keywords:
Naïve bayes classifier, sentiment analysis, support vector machine

Main Article Content

Rachell Aprinastya
Department of Information System, Universitas Islam Negeri Sultan Syarif Kasim Riau
Muhammad Jazman
Department of Information System, Universitas Islam Negeri Sultan Syarif Kasim Riau
Syaifullah Syaifullah
Department of Information System, Universitas Islam Negeri Sultan Syarif Kasim Riau
Medyantiwi Rahmawita
Department of Information System, Universitas Islam Negeri Sultan Syarif Kasim Riau
Syafril Siregar
Department of Information System, Universitas Islam Negeri Sultan Syarif Kasim Riau
Eki Saputra
Department of Information System, Universitas Islam Negeri Sultan Syarif Kasim Riau

Abstract

This study evaluates the performance of Support Vector Machine (SVM) and Naïve Bayes Classifier (NBC) algorithms in analyzing user sentiments regarding the popular online game Genshin Impact. The dataset comprises reviews sourced from the Google Play Store and App Store in both Indonesian and English, reflecting linguistic diversity. Preprocessing techniques such as tokenization, stemming, and Term Frequency-Inverse Document Frequency (TF-IDF) were employed to enhance data quality. Sentiments were classified as positive, neutral, or negative using TextBlob-assisted processes. Results demonstrate that NBC outperformed SVM across all metrics, with an average accuracy of 71% compared to 63%. Notably, sentiment analysis on English datasets consistently achieved higher accuracy than on Indonesian datasets, emphasizing the challenges posed by the linguistic complexity of Indonesian. This research underscores the critical role of language-specific adaptations in improving machine learning algorithms for multilingual sentiment analysis. The findings provide actionable insights for optimizing user engagement through enhanced game feedback mechanisms.

Article Details

How to Cite
Aprinastya, R., Jazman, M. ., Syaifullah, S., Rahmawita, M., Siregar, S., & Saputra, E. (2024). Comparative Analysis of Naïve Bayes Classifier and Support Vector Machine for Multilingual Sentiment Analysis: Insights from Genshin Impact User Reviews. JUSIFO (Jurnal Sistem Informasi), 10(2), 117-126. https://doi.org/10.19109/jusifo.v10i2.24876
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Vol. 10 No. 2 (2024): JUSIFO (Jurnal Sistem Informasi) | December 2024
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How to Cite

Aprinastya, R., Jazman, M. ., Syaifullah, S., Rahmawita, M., Siregar, S., & Saputra, E. (2024). Comparative Analysis of Naïve Bayes Classifier and Support Vector Machine for Multilingual Sentiment Analysis: Insights from Genshin Impact User Reviews. JUSIFO (Jurnal Sistem Informasi), 10(2), 117-126. https://doi.org/10.19109/jusifo.v10i2.24876

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