Comparative sentiment analysis on starbucks boycott related to Israel's genocide using svm, naïve bayes, and knn methods
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Abstract
The #BoycottStarbucks movement emerged in response to allegations of Starbucks' involvement in supporting the genocide carried out by Israel in Palestine. This research aims to analyze public sentiment towards Starbucks regarding its alleged involvement in the Israeli genocide. Data was collected through crawling English-language comments on a YouTube channel named Firstpost, which discussed the boycott of Starbucks. Three classification methods used were Naive Bayes, KNN, and SVM. The research findings indicate that the SVM model had the highest accuracy (82%) compared to Naive Bayes (75%) and KNN (56%). The distribution of sentiment in the comment data was dominated by neutral sentiment (44.5%), followed by positive sentiment (37.3%) and negative sentiment (18.2%). However, positive comments were also significant, indicating support or interest in the boycott movement. The hypotheses proposed supported the finding that public sentiment towards Starbucks tends to be negative and supportive of the boycott movement regarding its alleged involvement in the Israeli genocide. The research also concludes that various public opinions are reflected in neutral, positive, and negative comments, indicating that the boycott of Starbucks still attracts significant attention and discussion.
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References
M. Khoiruman dan A. Wariat, “Analisa Motivasi Boikot (Boycott Motivation) Terhadap Produk Mc Donald Di Surakarta Pasca Serangan Israel Ke Palestina,” J. Manajemen, Bisnis dan Pendidik., vol. 10, no. 2, hal. 247–257, 2023, [Daring]. Tersedia pada: https://e-journal.stie-aub.ac.id/index.php/excellent.
M. W. David M. Halbfinger dan S. Erlanger, “Is B.D.S. Anti-Semitic? A Closer Look at the Boycott Israel Campaign,” The New York Times, 2019. https://www.nytimes.com/2019/07/27/world/middleeast/bds-israel-boycott-antisemitic.html.
J. M. Hitchcock, “A Rhetorical Frame Analysis of Palestinian-Led Boycott, Divestment, Sanctions (BDS) Movement Discourse,” 2020, doi: 10.25777/gq1b-4m33.
S. P. Pandey dan M. Y. Ahmad, “An Analysis of Consumer Motivations for Boycott,” J. Univ. Shanghai Sci. Technol., vol. 22, no. 10, hal. 2016–2038, 2020.
A. Kurniawan, A. Hidayat, dan B. T. Andika, “Pengaruh Persepsi Kegunaan dan Persepsi Kemudahan terhadap Kepercayaan dan Kepuasan Konsumen pada Aplikasi Dompet Digital,” Indones. J. Econ. Business, Accounting, Manag., vol. 01, no. 03, hal. 71–84, 2023.
L. Wang, Y. Huang, dan M. K. Omar, “Analysis of Blended Learning Model Application Using Text Mining Method State of Art,” Int. J. Emerg. Technol. Learn., vol. 16, no. 01, hal. 172–187, 2021, doi: https://doi.org/10.3991/ijet.v16i01.19823 Lin.
N. L. P. C. Savitri, R. A. Rahman, R. Venyutzky, dan N. A. Rakhmawati, “Analisis Klasifikasi Sentimen Terhadap Sekolah Daring pada Twitter Menggunakan Supervised Machine Learning,” J. Tek. Inform. dan Sist. Inf., vol. 7, no. April, hal. 47–58, 2021, doi: http://dx.doi.org/10.28932/jutisi.v7i1.3216.
M. R. Ningsih dan Jumanto, “Sentiment Analysis on SocialMedia Using TF-IDF Vectorization and H2O Gradient Boosting for Student Anxiety Detection,” Sci. J. Informatics, vol. 11, no. 4, hal. 1137–1144, 2024, doi: 10.15294/sji.v11i4.20582.
M. R. Ningsih, “Classification Email Spam using Naive Bayes Algorithm and Chi-Squared Feature Selection,” vol. 9, no. 1, hal. 74–87, 2024.
E. W. Sholeha, S. Yunita, R. Hammad, V. C. Hardita, dan Kaharuddin, “Analisis Sentimen Pada Agen Perjalanan Online Menggunakan Naïve Bayes dan K-Nearest Neighbor ( Sentiment Analysis of Online Travel Agent Using Naïve Bayes,” JTIM J. Teknol. Inf. dan Multimed., vol. 3, no. 4, hal. 203–208, 2022.
M. R. Ma’arif, “Perbandingan Naïve Bayes Classifier Dan Support Vector Machine Untuk Klasifikasi Judul Artikel,” JISKA (Jurnal Inform. Sunan Kalijaga), vol. 1, no. 2, hal. 90–93, 2016, doi: https://doi.org/10.14421/jiska.2016.12-05.
A. H. Siregar, A. A. Tumanggor, dan A. Rahmadani, “Penerapan K-Nearest Neighbors ( KNN ) dalam Memprediksi dan Menghitung Akurasi Data Penyakit Stroke,” J. Penelit. Rumpun Ilmu Tek., vol. 2, no. 4, hal. 146–154, 2023, doi: https://doi.org/10.55606/juprit.v2i4.3040.
Legito et al., “Implementation K-Nearest Neighbor Algorithm for Sentiment Analysis on Khilafah and Radicalism Issues in Indonesia,” Indones. J. Mach. Learn. Comput. Sci., vol. 3, no. 2, hal. 324–330, 2023, doi: https://doi.org/10.57152/malcom.v3i2.893.
A. S. Nugroho, A. B. Witarto, dan D. Handoko, “Support Vector Machine,” Proceeding Indones. Sci. Meet. Cent. Japan, 2003, [Daring]. Tersedia pada: https://www.academia.edu/24381027/Support_Vector_Machine_Teori_dan_Aplikasinya_dalam_Bioinformatika_1.
P. Tan dan M. Steinbach, “Introduction to Data Mining Instructor ’ s Solution Manual,” 2006.
H. S. Ginting, K. M. Lhaksmana, dan D. T. Murdiansyah, “Klasifikasi Sentimen Terhadap Bakal Calon Gubernur Jawa Barat 2018 di Twitter Menggunakan Naive Bayes,” e-Proceeding Eng., vol. 5, no. 1, hal. 1793–1802, 2018.
S. Nurul, J. Fitriyyah, N. Safriadi, dan E. E. Pratama, “Analisis Sentimen Calon Presiden Indonesia 2019 dari Media Sosial Twitter Menggunakan Metode Naive Bayes,” J. Edukasi dan Penelit. Inform., vol. 5, no. 3, hal. 279–285, 2019.