Twitter Sentiment Analysis of Electric Vehicle Subsidy Policy using Naïve Bayes Algorithm
DOI:
https://doi.org/10.31258/jsmds.v1i1.3Keywords:
electric vehicle, naïve bayes, sentiment analysis, subsidy policy, twitterAbstract
This research aims to apply the Naïve Bayes classifier in Indonesian-language sentiment analysis, regarding electric vehicle subsidy policies using Twitter data with the query 'subsidi kendaraan listrik'. The stages of analysis include data pre-processing, tokenization, stemming, forming the Naïve Bayes model, and evaluating model performance using accuracy, precision, and recall. The SMOTE technique is used to deal with class imbalances, in which the majority of negative sentiments towards the policy is 66%. The results obtained from the 10-fold Cross Validation with the binary classification (positive and negative sentiment) show that the accuracy value of the model is 69.49%, with precision and recall values of 53.27% and 74.26%.
References
Afif, A. S., & Pratama, A. R. (2021). Analisis Sentimen Kebijakan Pendidikan di Masa Pandemi COVID-19 dengan CrowdTangle di Instagram. Automata. https://journal.uii.ac.id/AUTOMATA/article/view/19429
Aggarwal, C. C. (2022). An Introduction to Text Analytics. In Machine Learning for Text (pp. 1–17). Springer.
Berrar, D. (2019). Cross-Validation.
Elreedy, D., & Atiya, A. F. (2019). A comprehensive analysis of synthetic minority oversampling technique (SMOTE) for handling class imbalance. Information Sciences, 505, 32–64.
Kawani, G. P. (2019). Implementasi Naive Bayes. Journal of Informatics, Information System, Software Engineering and Applications (INISTA), 1(2), 73–81. https://doi.org/10.20895/inista.v1i2.73
Kurniawan, I., & Susanto, A. (2019). Implementasi Metode K-Means dan Naïve Bayes Classifier untuk Analisis Sentimen Pemilihan Presiden (Pilpres) 2019. Eksplora Informatika, 9(1), 1–10. https://doi.org/10.30864/eksplora.v9i1.237
Nur, A. I., & Kurniawan, A. D. (2021). Proyeksi Masa Depan Kendaraan Listrik di Indonesia: Analisis Perspektif Regulasi dan Pengendalian Dampak Perubahan Iklim yang Berkelanjutan. Jurnal Hukum Lingkungan Indonesia, 7(2), 197–220. https://doi.org/10.38011/jhli.v7i2.260
Ramadhon, M. I. (2020). Analisis Sentimen Terhadap Pemindahan Ibu Kota Indonesia Pada Media Sosial Twitter Menggunakan Metode Algoritma K-Nearest Neighbor (K-Nn).
Thabtah, F., Hammoud, S., Kamalov, F., & Gonsalves, A. (2020). Data imbalance in classification: Experimental evaluation. Information Sciences, 513, 429–441.
Vembandasamyp, K., Sasipriyap, R. R., & Deepap, E. (2015). Heart Diseases Detection Using Naive Bayes Algorithm. IJISET-International Journal of Innovative Science, Engineering & Technology, 2(9), 1–4. www.ijiset.com
Wati, R. (2016). Penerapan Algoritma Genetika Untuk Seleksi Fitur Pada Analisis Sentimen Review Jasa Maskapai Penerbangan Menggunakan Naive Bayes. Jurnal Evolusi, 4(1), 25– 31.
Zhu, Z., Blanke, U., Calatroni, A., & Tröster, G. (2013). Human activity recognition using social media data. 12th International Conference on Mobile and Ubiquitous Multimedia, MUM 2013. https://doi.org/10.1145/2541831.2541852
