ANALISIS SENTIMEN TERHADAP GAME GENSHIN IMPACT MENGGUNAKAN BERT

  • Ryo Kusnadi Universitas Internasional Batam
  • Yusuf Yusuf Universitas Internasional Batam
  • Andriantony Andriantony Universitas Internasional Batam
  • Richard Ardian Yaputra Universitas Internasional Batam
  • Melna Caintan Universitas Internasional Batam

Abstract

By huge improvement of Internet Services on social networking, there are a lot data that were streamly made in every time. Recently, sentiment analysis by using online reviews and messages has become a popular research issue in Natural Langauage Processing field. Over the years, online game have become inseparable thing for most of us, especially in the widespread economic disruption caused by the Covid-19. Genshin Impact is one of the well-known game that developed by miHoYo. This research focused on sentiment analysis with the purpose to find out whether the respected review that scraps from google play store has a neutral, positive or negative sentiment so it will be helpful for afterward game improvement. An autonomus sentiment analysis classification process is required to reduce human error. But, it is hard to get studies that explore about the extraction features and the deep learning models that fit with this case, especially in the business game. This research process stage is data scrapping through the google play store, and using Bidirectional Encoder Representations from Transformers (BERT) as the machine learning model.

Keywords: Sentiment Analysis, Classification, BERT, Data Science

References

D. L. King, P. H. Delfabbro, J. Billieux, and M. N. Potenza, “Problematic Online Gaming and The COVID-19 Pandemic,” J. Behav. Addict., vol. 9, no. 2, 2020.

S. Chakraborty, I. Mobin, A. Roy, and M. H. Khan, “Rating Generation of Video Games using Sentiment Analysis and Contextual Polarity from Microblog,” Proc. Int. Conf. Comput. Tech. Electron. Mech. Syst. CTEMS 2018, pp. 157–161, 2018, doi: 10.1109/CTEMS.2018.8769149.

L. Yang, Y. Li, J. Wang, and R. S. Sherratt, “Sentiment Analysis for E-Commerce Product Reviews in Chinese Based on Sentiment Lexicon and Deep Learning,” IEEE Access, vol. 8, pp. 23522–23530, 2020, doi: 10.1109/ACCESS.2020.2969854.

Y. Wang, K. T. Kim, B. J. Lee, and H. Y. Youn, “Word clustering based on POS feature for efficient twitter sentiment analysis,” Human-centric Comput. Inf. Sci., vol. 8, no. 1, 2018, doi: 10.1186/s13673-018-0140-y.

D. Blazquez and J. Domenech, “Big Data sources and methods for social and economic analyses,” Technol. Forecast. Soc. Change, vol. 130, no. March, pp. 99–113, 2018, doi: 10.1016/j.techfore.2017.07.027.

S. Pradha, M. N. Halgamuge, and N. Tran Quoc Vinh, “Effective text data preprocessing technique for sentiment analysis in social media data,” Proc. 2019 11th Int. Conf. Knowl. Syst. Eng. KSE 2019, pp. 1–8, 2019, doi: 10.1109/KSE.2019.8919368.

K. Fithriasari, I. Hariastuti, and K. S. Wening, “Handling Imbalance Data in Classification Model with Nominal Predictors,” Int. J. Comput. Sci. Appl. Math., vol. 6, no. 1, p. 33, 2020, doi: 10.12962/j24775401.v6i1.6643.

Z. Jianqiang and G. Xiaolin, “Comparison research on text pre-processing methods on twitter sentiment analysis,” IEEE Access, vol. 5, no. c, pp. 2870–2879, 2017, doi: 10.1109/ACCESS.2017.2672677.

S. Wahyu Handani, D. Intan Surya Saputra, Hasirun, R. Mega Arino, and G. Fiza Asyrofi Ramadhan, “Sentiment analysis for go-jek on google play store,” J. Phys. Conf. Ser., vol. 1196, no. 1, 2019, doi: 10.1088/1742-6596/1196/1/012032.

J. Devlin, M. W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of deep bidirectional transformers for language understanding,” NAACL HLT 2019 - 2019 Conf. North Am. Chapter Assoc. Comput. Linguist. Hum. Lang. Technol. - Proc. Conf., vol. 1, no. Mlm, pp. 4171–4186, 2019.

J. Howard and S. Ruder, “Universal language model fine-tuning for text classification,” ACL 2018 - 56th Annu. Meet. Assoc. Comput. Linguist. Proc. Conf. (Long Pap., vol. 1, pp. 328–339, 2018, doi: 10.18653/v1/p18-1031.

M. E. Peters et al., “Deep contextualized word representations,” NAACL HLT 2018 - 2018 Conf. North Am. Chapter Assoc. Comput. Linguist. Hum. Lang. Technol. - Proc. Conf., vol. 1, pp. 2227–2237, 2018, doi: 10.18653/v1/n18-1202.

Z. Gao, A. Feng, X. Song, and X. Wu, “Target-dependent sentiment classification with BERT,” IEEE Access, vol. 7, pp. 154290–154299, 2019, doi: 10.1109/ACCESS.2019.2946594.

D. Kondratyuk and M. Straka, “75 Languages, 1 Model: Parsing Universal Dependencies Universally,” arXiv, pp. 2779–2795, 2019.

E. S. Palupi and S. M. Pahlevi, “Klasifikasi Opportunity Menggunakan Algoritma C4.5, C4.5 dan Naive Bayes Berbasis Particle Swarm Optimization,” Inti Nusa Mandiri, vol. 14, no. 2, pp. 133–138, 2020.

Ainurrohmah, “Akurasi Algoritma Klasifikasi pada Software Rapidminer dan Weka,” vol. 4, pp. 493–499, 2021.

E. Sutoyo and M. A. Fadlurrahman, “Penerapan SMOTE untuk Mengatasi Imbalance Class dalam Klasifikasi Television Advertisement Performance Rating Menggunakan Artificial Neural Network,” JEPIN (Jurnal Edukasi dan Penelit. Inform., vol. 6, no. 3, pp. 379–385, 2020.

Y. Guan, J. Leng, C. Li, Q. Chen, and M. Guo, “How Far Does BERT Look At: Distance-based Clustering and Analysis of BERT’s Attention,” pp. 3853–3860, 2021, doi: 10.18653/v1/2020.coling-main.342.

R. A. Pangestu, B. Rahmat, and F. T. Anggraeny, “Implementasi Algoritma CNN untuk Klasifikasi Citra Lahan dan Perhitungan Luas,” Inform. dan Sist. Inf., vol. 1, no. 1, pp. 166–174, 2020.

X. Li, S. Chen, Y. Xia, and J. Yang, “Understanding the disharmony between weight normalization family and weight decay: ε−shifted L2 regularizer,” arXiv, vol. 1, 2019.

Published
2021-07-08
How to Cite
Kusnadi, R., Yusuf, Y., Andriantony, A., Ardian Yaputra, R., & Caintan, M. (2021). ANALISIS SENTIMEN TERHADAP GAME GENSHIN IMPACT MENGGUNAKAN BERT. Rabit : Jurnal Teknologi Dan Sistem Informasi Univrab, 6(2), 122-129. https://doi.org/10.36341/rabit.v6i2.1765
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Articles
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