- Sentiment Analysis,
- BERT Transformer,
- Israel-Palestine Conflict,
- Social Media,
- NLP
Copyright (c) 2024 Syaiful Mulki Almubarok Renhoran, Hamzah Setiawan
This work is licensed under a Creative Commons Attribution 4.0 International License.
Abstract
General background Israel-Palestine conflict has drawn significant global attention, particularly in how it is perceived and discussed on social media platforms. Specific background understanding public sentiment surrounding such geopolitical issues is crucial for media monitoring, diplomatic efforts, and reputation management. Knowledge gap previous sentiment analysis studies often lack the ability to accurately handle multilingual and context-rich datasets, especially in analyzing neutral sentiments, which are commonly overlooked. This study aims to apply the Bidirectional Encoder Representations from Transformers (BERT) model to analyze public sentiment towards the Israel-Palestine conflict on the X platform, focusing on Indonesian users. Results Using BERT, the model achieved 93% accuracy, with a precision of 0.95, recall of 0.93, and F1-score of 0.94. The model performed well in predicting positive and negative sentiments but showed room for improvement in handling neutral sentiment. Novelty this study introduces the implementation of the BERT Transformer model for the multilingual and context-sensitive sentiment analysis of tweets, specifically addressing a high-stakes geopolitical conflict. Implications the findings demonstrate the potential for using advanced natural language processing techniques like BERT for monitoring public opinion, brand management, and detecting societal tensions on social media, offering valuable insights for stakeholders involved in conflict resolution and diplomatic strategies.
Highlights:
- Achieved 93% accuracy in sentiment analysis using BERT on X platform.
- Identified strengths in predicting positive/negative sentiments, with challenges in neutral sentiment.
- Demonstrated BERT’s effectiveness in handling complex geopolitical social media data.
Keywords: Sentiment Analysis, BERT Transformer, Israel-Palestine Conflict, Social Media, NLP
References
[2] V. Chandradev, I. Made, A. Dwi Suarjaya, I. Putu, and A. Bayupati, "Analisis Sentimen Review Hotel Menggunakan Metode Deep Learning BERT," n.d.
[3] N. Karimah and A. Baita, "Multi-Aspect Sentiment Analysis pada Review Film Menggunakan Metode Bidirectional Encoder Representations From Transformers (BERT)," Jurnal Sistem Komputer, vol. 13, no. 1, pp. 1–10, 2024. doi: 10.34010/komputika.v13i1.11098.
[4] J. Khatib Sulaiman, M. Putri, T. Edy Sutanto, and S. Inna, "Studi Empiris Model BERT dan DistilBERT: Analisis Sentimen pada Pemilihan Presiden Indonesia," Indonesian Journal of Computer Science, vol. 12, no. 5, pp. 1–10, 2023.
[5] N. Putu, V. D. Saraswati, N. Yudistira, and P. P. Adikara, "Analisis Sentimen terhadap Perundungan Siber pada X Menggunakan Algoritma Bidirectional Encoder Representations from Transformer (BERT)," Jurnal PTIIK, vol. 7, no. 2, pp. 1–10, 2023.
[6] T. Rifqah, R. Tanjung, A. Satria, R. I. Rahmayani Tanjung, and J. J. Sihotang, "Konflik Palestina: Jihad Netizen Indonesia, Solidaritas Atau Pelanggaran Hukum," vol. 2, no. 2, pp. 229–238, 2024. doi: 10.51903/jaksa.v1i3.14670.
[7] N. Sofi, T. Sulistyorini, and M. Nazaruddin, "Analisis Sentimen Masyarakat Pengguna Media Sosial X Terhadap MotoGP Mandalika Lombok Menggunakan Metode Bidirectional Encoder Representation From Transformers (BERT)," vol. 1, pp. 1–10, 2023.
[8] R. Sagita Dewi Manajemen Bisnis Syari and S. Hamfara Yogyakarta, "Pengaruh Konflik Palestina-Israel Terhadap Perekonomian Dunia," vol. 2, 2024.
[9] N. Sofi, T. Sulistyorini, and M. Nazaruddin, "Analisis Sentimen Masyarakat Pengguna Media Sosial Twitter Terhadap MotoGP Mandalika Lombok Menggunakan Metode Bidirectional Encoder Representation From Transformers (BERT)," vol. 1, pp. 1–10, 2023.
[10] A. Tri Wicaksono, A. Arbi, N. Badrotin Jabbar, A. Fajruddin Fatwa, J. Ahmad Yani, K. Wonocolo No, and S. Korespondensi Penulis, "Problematika ICC Dalam Menjatuhkan Sanksi Kepada Israel Dalam Perspektif Hukum Internasional," JHPIS, vol. 3, no. 1, pp. 207–224, 2024. doi: 10.55606/jhpis.v3i1.3210.
[11] R. Trisnawati Manajemen Bisnis Syariah and S. Hamfara, "Boikot dan Aktivisme: Perilaku Konsumen dalam Isu Konflik Israel-Palestina," n.d.
[12] E. H. Muktafin, K. Kusrini, and E. T. Luthfi, "Analisis Sentimen pada Ulasan Pembelian Produk di Marketplace Shopee Menggunakan Pendekatan Natural Language Processing," Jurnal Eksplora Informatika, vol. 10, no. 1, pp. 32–42, 2020.
[13] S. Fathoniah and C. Rozikin, "Analisis Sentimen Masyarakat terhadap Teroris dalam Media Sosial Twitter menggunakan NLP," Jurnal Ilmiah Wahana Pendidikan, vol. 8, no. 13, pp. 412–419, 2022.
[14] N. Munasatya and S. Novianto, "Natural Language Processing untuk Analisis Sentimen Presiden Jokowi Menggunakan Multi Layer Perceptron," Techno. Com, vol. 19, no. 3, 2020.
[15] R. V. Sumendapa and I. B. M. Mahendraa, "Membandingkan Analisis Sentimen Review Pelanggan Shopee Dan Tokopedia Menggunakan Google's NLP API," Jurnal Elektronik Ilmu Komputer Udayana, p-ISSN 2301-5373, 2023.