Sentiment Analysis of Genshin Impact on X: Mental Health Implications Using TF-IDF and Support Vector Machine

Authors

  • Sava Irhab Atma Jaya Department of Informatics Engineering, Faculty of Computer Science, Universitas Dian Nuswantoro, Semarang, Indonesia
  • Junta Zeniarja Department of Informatics Engineering, Faculty of Computer Science, Universitas Dian Nuswantoro, Semarang, Indonesia

DOI:

10.33395/sinkron.v8i3.13716

Keywords:

Sentiment Analysis; Support Vector Machine; TF-IDF; Genshin Impact; Mental Health;

Abstract

Genshin Impact are now an integral part of daily life for many, potentially influencing mental well-being. Sentiment analysis window into these emotional effects, especially given the varied findings on gaming's impact on mental health. Analyzing X responses Genshin Impact using Support Vector Machine crucial, given its effectiveness in sentiment analysis. This study aims to deepen our understanding game's psychological impact and support development mental health interventions for gamers. The SVM classification report shows promising precision: 0.68 for Negative, 0.63 for Neutral, and 0.72 for Positive sentiment. However, recall rates favor Positive reviews (0.87) over Negative (0.56) and Neutral (0.51), reflected in the F1 score, highest for Positive sentiment at 0.79. With 174 Negative, 216 Neutral, and 333 Positive support counts, model achieved an overall accuracy of 0.69, effectively classifying Genshin Impact reviews based on sentiment. Analysis findings suggest a prevalence of positive opinions, indicating widespread player satisfaction with the game.

GS Cited Analysis

Downloads

Download data is not yet available.

References

Arias, F., Zambrano Nunez, M., Guerra-Adames, A., Tejedor-Flores, N., & Vargas-Lombardo, M. (2022). Sentiment Analysis of Public Social Media as a Tool for Health-Related Topics. IEEE Access, 10(June), 74850–74872. https://doi.org/10.1109/ACCESS.2022.3187406

Iram, & Aggarwal, H. (2020). Time series analysis of pubg and tiktok applications using sentiments obtained from social media-twitter. Advances in Mathematics: Scientific Journal, 9(6), 4047–4057. https://doi.org/10.37418/amsj.9.6.86

Jeong, B., Lee, J., Kim, H., Gwak, S., Kim, Y. K., Yoo, S. Y., Lee, D., & Choi, J. S. (2022). Multiple-Kernel Support Vector Machine for Predicting Internet Gaming Disorder Using Multimodal Fusion of PET, EEG, and Clinical Features. Frontiers in Neuroscience, 16(June). https://doi.org/10.3389/fnins.2022.856510

Kaur, H., Ahsaan, S. U., Alankar, B., & Chang, V. (2021). A Proposed Sentiment Analysis Deep Learning Algorithm for Analyzing COVID-19 Tweets. Information Systems Frontiers, 23(6), 1417–1429. https://doi.org/10.1007/s10796-021-10135-7

Krittanawong, C., Virk, H. U. H., Katz, C. L., Kaplin, S., Wang, Z., Gonzalez-Heydrich, J., Storch, E. A., & Lavie, C. J. (2022). Association of Social Gaming with Well-Being (Escape COVID-19): A Sentiment Analysis. American Journal of Medicine, 135(2), 254–257. https://doi.org/10.1016/j.amjmed.2021.10.010

Mathematics, A. (2022). Gamification + Hci + Cmc: Effects of Persuasive Video Games on Consumers’ Mental and Physical Health. December, 1–23.

Olajuwon, S. M. R., Kusrini, K., & Kusnawi, K. (2024). Analyzing Public Sentiment Regarding the Qatar 2023 World Cup Debate Using TF-IDF and K-Nearest Neighbor Weighting. Sinkron, 8(2), 679–688. https://doi.org/10.33395/sinkron.v8i2.13275

Oyebode, O., Alqahtani, F., & Orji, R. (2020). Using Machine Learning and Thematic Analysis Methods to Evaluate Mental Health Apps Based on User Reviews. IEEE Access, 8, 111141–111158. https://doi.org/10.1109/ACCESS.2020.3002176

Patel, R., & Passi, K. (2020). Sentiment Analysis on Twitter Data of World Cup Soccer Tournament Using Machine Learning. Internet of Things, 1(2), 218–239. https://doi.org/10.3390/iot1020014

Prawida, R., & Wahyuningsih, T. (2023). The Analysis of Gacha Game Addiction on the Players’ Personal Monthly Expenses. Undergraduate Conference on Language, Literature, and Culture (UNCLLE), 3(1), 372–382. http://publikasi.dinus.ac.id/index.php/unclle

Primandani Arsi, Pungkas Subarkah, & Bagus Adhi Kusuma. (2023). Analisis Sentimen Game Genshin Impact pada Play Store Menggunakan Naïve Bayes Clasifier. Jurnal Ilmiah Teknik Mesin, Elektro Dan Komputer, 3(1), 161–170. https://doi.org/10.51903/juritek.v3i1.1962

Shofiya, C., & Abidi, S. (2021). Sentiment analysis on covid-19-related social distancing in Canada using twitter data. International Journal of Environmental Research and Public Health, 18(11). https://doi.org/10.3390/ijerph18115993

Shu, T., Wang, Z., Jia, H., Zhao, W., Zhou, J., & Peng, T. (2022). Consumers’ Opinions towards Public Health Effects of Online Games: An Empirical Study Based on Social Media Comments in China. International Journal of Environmental Research and Public Health, 19(19). https://doi.org/10.3390/ijerph191912793

Singh, A. K. (2022). Sentiment Analysis of Dota 2 videogame chat in context of Cyber-bullying. https://www.vuelio.com/uk/resources/white-papers/pr-media-travel-trends-2021/

Tunca, S., Sezen, B., & Wilk, V. (2023). An exploratory content and sentiment analysis of the guardian metaverse articles using leximancer and natural language processing. Journal of Big Data, 10(1). https://doi.org/10.1186/s40537-023-00773-w

Downloads


Crossmark Updates

How to Cite

Jaya, S. I. A. ., & Junta Zeniarja. (2024). Sentiment Analysis of Genshin Impact on X: Mental Health Implications Using TF-IDF and Support Vector Machine. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 8(3), 1589-1599. https://doi.org/10.33395/sinkron.v8i3.13716

Issue

Vol. 8 No. 3 (2024): Research Artikel Volume 8 Issue 3, July 2024

Section

Articles

License

Copyright (c) 2024 Sava Irhab Atma Jaya, Junta Zeniarja

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Crossref
Scopus
Google Scholar
Europe PMC

Most read articles by the same author(s)