SENTIMENT ANALYSIS OF LEAGUE OF LEGENDS: WILD RIFT REVIEWS ON GOOGLE PLAY USING NAÃVE BAYES CLASSIFIER
DOI:
https://doi.org/10.21107/kursor.v12i01.328Keywords:
Sentiment Analysis, Naive Bayes Classifier, TF-IDF, BOW, Game reviews, League of Legend.Abstract
League of Legends: Wild Rift is a mobile game with more than 48 million downloads worldwide. The game publishers could earn profit from selling item in the game (in-app purchases). Performance of player and players' impressions during the first week usually determine whether players would made in-app purchases or not. Therefore, it is important to understand the player opinions so that the game publisher could encourage the players to increase the in-app purchases. Therefore, this research utilized sentiment analysis to study the player opinions about the League of Legends: Wild Rift game based on the reviews given by the players on the Google Play Store. The sentiment analysis was applied by using Naive Bayes Classifier (NBC) algorithm which was well known for achieving good accuracy in the sentiment analysis task. In addition, data preprocessing and feature extraction should be carried out properly to increase the accuracy of the classifier. Therefore, this research investigated the impact of using stemming and transformation of informal words into formal words in the preprocessing stages, then compared two feature extraction algorithms, namely Term Frequency – Inverse Document Frequency (TF-IDF) and Bag of Words (BOW). From the experiment, it was found that the use of stemming could decrease the accuracy of the classifier, but the use of transformation of non-standard words into standard words could improve the performance of the classifier, for both feature extractions, BOW and TF-IDF. In this case, BOW feature extraction was able to achieve better performance, compared to TF-IDF. The best model was achieved when not using stemming, applying the transformation of informal words into formal words, and using BOW bigram feature extraction, with the accuracy of 79,3%, precision of 82.10%, recall of 83.50%, and f1-score of 82,8.10%.
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[3] T. U. Haque, N. N. Saber, and F. M. Shah, “Sentiment analysis on large scale Amazon product reviews,†2018 IEEE International Conference on Innovative Research and Development, ICIRD 2018, pp. 1–6, Jun. 2018, doi: 10.1109/ICIRD.2018.8376299.
[4] S. W. H. Kwok, S. K. Vadde, and G. Wang, “Tweet Topics and Sentiments Relating to COVID-19 Vaccination Among Australian Twitter Users: Machine Learning Analysis,†J Med Internet Res, vol. 23, no. 5, May 2021, doi: 10.2196/26953.
[5] B. Kurniawan, S. Effendi, and O. S. Sitompul, “Klasifikasi Konten Berita Dengan Metode Text Mining,†Dunia Teknologi Informasi - Jurnal Online, vol. 1, no. 1, Dec. 2012, Accessed: Sep. 26, 2022. [Online]. Available: https://jurnal.usu.ac.id/index.php/duniait/article/view/409
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[7] S. Fransiska, Rianto, and A. I. Gufroni, “Sentiment Analysis Provider by.U on Google Play Store Reviews with TF-IDF and Support Vector Machine (SVM) Method,†Scientific Journal of Informatics, vol. 7, no. 2, pp. 2407–7658, 2020, [Online]. Available: http://journal.unnes.ac.id/nju/index.php/sji
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[10] A. Singh, M. N. Halgamuge, and R. Lakshmiganthan, “Impact of Different Data Types on Classifier Performance of Random Forest, Naïve Bayes, and K-Nearest Neighbors Algorithms,†2017. [Online]. Available: www.ijacsa.thesai.org
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[12] A. K. Sharma, S. Kumar Prajapat, and M. Aslam, “A Comparative Study between Naïve Bayes and Neural Network (MLP) Classifier for Spam Email Detection,†International Journal of Computer Applications (IJCA), pp. 12–16, 2014.
[13] H. Simorangkir and K. M. Lhaksmana, “Analisis Sentimen Pada Twitter Untuk Games Online Mobile Legends Dan Arena Of Valor Dengan Metode Naïve Bayes Classifier,†eProceedings of Engineering, vol. 5, no. 3, Dec. 2018, Accessed: Sep. 25, 2022. [Online]. Available: https://openlibrarypublications.telkomuniversity.ac.id/index.php/engineering/article/view/7448
[14] M. E. Permana, H. Ramadhan, I. Budi, A. B. Santoso, and P. K. Putra, “Sentiment analysis and topic detection of mobile banking application review,†in 2020 5th International Conference on Informatics and Computing, ICIC 2020, Institute of Electrical and Electronics Engineers Inc., Nov. 2020. doi: 10.1109/ICIC50835.2020.9288616.
[15] M. A. M. Salem and A. Y. A. Maghari, “Sentiment analysis of mobile phone products reviews using classification algorithms,†in Proceedings - 2020 International Conference on Promising Electronic Technologies, ICPET 2020, Institute of Electrical and Electronics Engineers Inc., Dec. 2020, pp. 84–88. doi: 10.1109/ICPET51420.2020.00024.
[16] A. Y. Maghari and J. H. Zendah, “Detecting Significant Events in Arabic Microblogs using Soft Frequent Pattern Mining,†Journal of Engineering Research and Technology, vol. 6, no. 1, Mar. 2019, Accessed: Oct. 03, 2022. [Online]. Available: https://journals.iugaza.edu.ps/index.php/JERT/article/view/5365
[17] G. Gautam and D. Yadav, “Sentiment analysis of twitter data using machine learning approaches and semantic analysis,†2014 7th International Conference on Contemporary Computing, IC3 2014, pp. 437–442, 2014, doi: 10.1109/IC3.2014.6897213.
[18] B. A. Alhaj and A. Y. A. Maghari, “Predicting User Entries by Using Data Mining Algorithms,†Proceedings - 2017 Palestinian International Conference on Information and Communication Technology, PICICT 2017, pp. 110–114, Sep. 2017, doi: 10.1109/PICICT.2017.24.
[19] Mhd. T. A. Bangsa, S. Priyanta, and Y. Suyanto, “Aspect-Based Sentiment Analysis of Online Marketplace Reviews Using Convolutional Neural Network,†IJCCS (Indonesian Journal of Computing and Cybernetics Systems), vol. 14, no. 2, p. 123, Apr. 2020, doi: 10.22146/ijccs.51646.
[20] N. A. Salsabila, Y. A. Winatmoko, A. A. Septiandri, and A. Jamal, “Colloquial Indonesian Lexicon,†Proceedings of the 2018 International Conference on Asian Language Processing, IALP 2018, pp. 226–229, Jan. 2019, doi: 10.1109/IALP.2018.8629151.
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[23] Jurafsky, D., & Martin, J. H. (2021). Speech and Language Processing An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition First Edition.
[24] Rahmawan, F. (2011). Implementasi Question Answering System pada Dokumen Bahasa Indonesia Menggunakan Metode N-Gram. http://repository.ipb.ac.id/handle/123456789/54453
[25] Bishop, C. M. (2006). Pattern Recognition and Machine Learning (M. Jordan, J. Kleinberg, & B. Scholkopf, Ed.). Springer.
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