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Aspect-Based Sentiment Analysis of KAI Access Reviews Using NBC and SVM
https://doi.org/10.22146/ijccs.68903 Huda Mustakim(1*), Sigit Priyanta(2)
(1) Undergraduate Program of Computer Science; FMIPA UGM, Yogyakarta
(2) Department of Computer Science and Electronics, FMIPA UGM, Yogyakarta
(*) Corresponding Author
Abstract
The existence of KAI Access from PT. KAI prove their sincerity in serving consumers in this modern era. However, many negative reviews found in Google Play Store. There has been research on the review, but the analysis stage still at document level so the aspect related to the application is not known clearly and structured. So it is necessary to do an aspect-based sentiment analysis to extract the aspects and the sentiment. This study aims to do an aspect-based sentiment analysis on user reviews of KAI Access using Naive Bayes Classifier (NBC) and Support Vector Machine (SVM), with 3 scenarios. Scenario 1 uses NBC with Multinomial Naive Bayes, scenario 2 uses SVM with default Sklearn library parameter, and scenario 3, uses SVM with hyperparameter tunning, while the data scrapped from Google Play Store. The results show the majority of user sentiment is negative for each aspect, with most discussed errors aspect shows the high system errors. The test results gives the best model from scenario 3 with an average accuracy 91.63%, f1-score 75.55%, precision 77.60%, and recall 74.47%.
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[1] N. Fikria, "Analisis Klasifikasi Sentimen Review Aplikasi E-Ticketing Menggunakan Metode Support Vector Machine Dan Asosiasi", Undergraduate, Universitas Islam Indonesia, 2018.
[2] B. Liu, Sentiment Analysis and Opinion Mining. Morgan & Claypool Publishers, pp. 1-167, 2012.
[3] S. Astuti, "Analisis Sentimen Berbasis Aspek Pada Aplikasi Tokopedia Menggunakan LDA dan Naïve Bayes", Undergraduate, UIN Syarif Hidayatullah, 2020.
[4] S. Ailiyya, "Analisis Sentimen Berbasis Aspek Pada Ulasan Aplikasi Tokopedia Menggunakan Support Vector Machine", Undergraduate, UIN Syarif Hidayatullah, 2020.
[5] M. Al-Smadi, O. Qawasmeh, M. Al-Ayyoub, Y. Jararweh, and B. Gupta. “Deep Recurrent neural network vs. support vector machine for aspect-based sentiment analysis of Arabic hotels’ reviews”. Journal of Computational Science, 27, 386–393, 2018. https://doi.org/10.1016/j.jocs.2017.11.006
[6] A. P. Rodrigues and N. N. Chiplunkar, "Aspect Based Sentiment Analysis on Product Reviews," 2018 Fourteenth International Conference on Information Processing (ICINPRO), 2018, pp. 1-6, doi: 10.1109/ICINPRO43533.2018.9096796
[7] C. Aggarwal, Data Mining, 1st ed. Springer, Cham, 2015. https://doi.org/10.1007/978-3-319-14142-8
[8] A. Wibawa, A. Kurniawan, D. Murti, R. Adiperkasa, S. Putra, S. Kurniawan, and Y. Nugraha. “Naïve Bayes Classifier for Journal Quartile Classification”. International Journal of Recent Contributions from Engineering, Science & IT (IJES), vol. 7, no. 2, 2019. https://doi.org/10.3991/ijes.v7i2.10659
[9] S. R. Wardhana, "Analisis sentimen pada opini pengguna Aplikasi Mobile untuk evaluasi faktor kebergunaan", Postgraduate, Institut Teknologi Sepuluh Nopember, 2017.
[10] A. Dinakaramani, F. Rashel, A. Luthfi, and R. Manurung, “Designing an Indonesian part of speech tagset and manually tagged Indonesian corpus,” in Proceedings of the International Conference on Asian Language Processing 2014, IALP 2014, Oct. 2014, pp. 66–69, doi: 10.1109/IALP.2014.6973519.
[11] P. Qi, Y. Zhang, Y. Zhang, J. Bolton, and C. D. Manning, “Stanza: A Python Natural Language Processing Toolkit for Many Human Languages,” Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, 2020.
[12] G. Buntoro, "Analisis Sentimen Calon Gubernur DKI Jakarta 2017 Di Twitter", INTEGER: Journal of Information Technology, vol. 2, no. 1, pp. 32-41, 2017. https://doi.org/10.31284/j.integer.2017.v2i1.95
[13] O. Heranova. “Synthetic Minority Oversampling Technique pada Averaged One Dependence Estimators untuk Klasifikasi Credit Scoring”. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), vol. 3, no. 3, pp. 443-450, 2019. https://doi.org/10.29207/resti.v3i3.1275
[14] M. S. Santos, J. P. Soares, P. H. Abreu, H. Araujo, and J. Santos, “Cross-Validation for Imbalanced Datasets: Avoiding Overoptimistic and Overfitting Approaches [Research Frontier],” IEEE Computational Intelligence Magazine, vol. 13, no. 4, pp. 59–76, 2018.
[15] J. Han, M. Kamber, and J. Pei. Data Mining: Concepts and Techniques. 3rd ed. Waltham: Elsevier, 2012. doi: 10.1016/C2009-0-61819-5.
DOI: https://doi.org/10.22146/ijccs.68903
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