ANALISIS SENTIMEN TERHADAP GAME GENSHIN IMPACT MENGGUNAKAN BERT
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.
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.
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Please find the rights and licenses in RABIT : Jurnal Teknologi dan Sistem Informasi Univrab. By submitting the article/manuscript of the article, the author(s) accept this policy.
The non-commercial use of the article will be governed by the Creative Commons Attribution license as currently displayed on Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
2. Author’s Warranties
The author warrants that the article is original, written by stated author(s), has not been published before, contains no unlawful statements, does not infringe the rights of others, is subject to copyright that is vested exclusively in the author and free of any third party rights, and that any necessary written permissions to quote from other sources have been obtained by the author(s).
3. User Rights
RABIT's spirit is to disseminate articles published are as free as possible. Under the Creative Commons license, RABIT permits users to copy, distribute, display, and perform the work for non-commercial purposes only. Users will also need to attribute authors and RABIT on distributing works in the journal.
4. Rights of Authors
Authors retain all their rights to the published works, such as (but not limited to) the following rights;
- Copyright and other proprietary rights relating to the article, such as patent rights,
- The right to use the substance of the article in own future works, including lectures and books,
- The right to reproduce the article for own purposes,
- The right to self-archive the article,
- The right to enter into separate, additional contractual arrangements for the non-exclusive distribution of the article's published version (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal (RABIT : Jurnal Teknologi dan Sistem Informasi Univrab).
If the article was jointly prepared by other authors, any authors submitting the manuscript warrants that he/she has been authorized by all co-authors to be agreed on this copyright and license notice (agreement) on their behalf, and agrees to inform his/her co-authors of the terms of this policy. RABIT will not be held liable for anything that may arise due to the author(s) internal dispute. RABIT will only communicate with the corresponding author.
This agreement entitles the author to no royalties or other fees. To such extent as legally permissible, the author waives his or her right to collect royalties relative to the article in respect of any use of the article by RABIT.
RABIT will publish the article (or have it published) in the journal if the article’s editorial process is successfully completed. RABIT's editors may modify the article to a style of punctuation, spelling, capitalization, referencing and usage that deems appropriate. The author acknowledges that the article may be published so that it will be publicly accessible and such access will be free of charge for the readers as mentioned in point 3.