Sentiment Analysis of Honkai Star Rail Game Reviews on Google Play Using the Naive Bayes Algorithm

Bagus Doang; Robi Aziz Zuama; Nila Hardi

doi:10.59934/jaiea.v5i2.2120

Authors

Bagus Doang Universitas Bina Sarana Informatika
Robi Aziz Zuama Universitas Bina Sarana Informatika
Nila Hardi Universitas Bina Sarana Informatika

DOI:

https://doi.org/10.59934/jaiea.v5i2.2120

Keywords:

Sentiment analysis, Honkai Star Rail, Machine Learning, Naïve Bayes

Abstract

Honkai Star Rail is a turn-based JRPG developed by COGNOSPHERE PTE. LTD. that has attracted a large number of players with over 10 million downloads and 339,000 reviews on the Google Play Store. While most reviews are positive, some users have expressed their dissatisfaction. Sentiment analysis is crucial for extracting insights from reviews without having to read the entire text. The objective of this research is to conduct sentiment analysis on Honkai Star Rail users using the Naïve Bayes algorithm. This research employs the Naïve Bayes method due to its simplicity and ability to produce accurate predictions. Review data was collected through scraping from the Google Play Store and analyzed using the TF-IDF technique for word weighting. The model testing results showed the highest accuracy on test data using split validation with a 75:25 ratio, achieving 84.7%. The evaluation of the confusion matrix for positive sentiment resulted in a precision of 100%, recall of 12%, and an F1-Score of 22%, while for negative sentiment, it resulted in a precision of 84%, recall of 100%, and an F1-Score of 92%. This research contributes by providing feedback for game developers to improve quality and enhance player satisfaction

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