Sentiment Analysis of the Israel-Palestine Conflict on X: Insights from the Indonesian Perspective using a Long Short-Term Memory Algorithm
Article Sidebar
Main Article Content
Abstract
The Israel-Palestine conflict which has persisted for decades drives mounting global interest that consequently influences public opinion worldwide. This article examines the sentiment analysis of X (Twitter) data pertaining to the conflict using the Long Short-Term Memory (LSTM) model. This study presents public reactions through an analysis of 1,700 tweets collected between May and July 2023 which encapsulate key recent developments. In this study, several steps were conducted, namely 1) crawling process to get raw data; 2) preprocessing: cleansing, case folding, tokenization, stop word removal, and stemming; 3) modelling and validation using the LSTM model; 4) model evaluation based on performance metrics to evaluate the ability of the classification model to distinguish between classes; 5) visualization of experimental results. The LSTM model is a modification of the recurrent neural network (RNN). The LSTM model has many advantages, including being able to remember a collection of information that has been stored for a long period of time, being able to delete information that is no longer relevant, and being more efficient in processing, predicting, and classifying data based on a certain time sequence. Another advantage is that LSTM's ability to identify temporal dependencies and contextual interactions in sequential data makes it suitable for social media text analysis. The model demonstrated success in sentiment classification on geopolitical topics with an impressive accuracy rate of 91%. The findings demonstrate deep learning's potential applications for sentiment analysis and offer insights into public opinion dynamics during times of international crises.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
All articles published in JIWE are licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) License. Readers are allowed to
- Share — copy and redistribute the material in any medium or format under the following conditions:
- Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use;
- NonCommercial — You may not use the material for commercial purposes;
- NoDerivatives — If you remix, transform, or build upon the material, you may not distribute the modified material.
References
“The Military Lessons of the Gaza War of May 2021”. Accessed: May 09, 2025. [Online]. Available: https://trendsresearch.org/insight/the-military-lessons-of-the-gaza-war-of-may-2021/
Md. E. Sharkar, M. J. Hosen, Md. Abdullah, S. Islam, S. Rana, and N. Sultana, “Sentiment Analysis of Israel-Palestine Conflict Comments Using Sentiment Intensity Analyzer and TextBlob,” in 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT), Jun. 2024, pp. 1–7. doi: 10.1109/ICCCNT61001.2024.10724497.
J. Choi and J. and Lee, “Enthusiasm” toward the other side matters: Emotion and willingness to express disagreement in social media political conversation,” The Social Science Journal, vol. 0, no. 0, pp. 1–17. doi: 10.1080/03623319.2021.1949548.
P. Gaur, S. Vashistha, and P. Jha, “Twitter Sentiment Analysis Using Naive Bayes-Based Machine Learning Technique”, In Sentiment Analysis and Deep Learning: Proceedings of ICSADL 2022, Eds., Singapore: Springer Nature, 2023, pp. 367–376. doi: 10.1007/978-981-19-5443-6_27.
M. H. Al-Areef, and K. Saputra S, “Analisis Sentimen Pengguna Twitter Mengenai Calon Presiden Indonesia Tahun 2024 Menggunakan Algoritma LSTM,” J. SAINTIKOM J. Sains Manaj. Inform. Dan Komput., vol. 22, no. 2, pp. 270, Aug. 2023. doi: 10.53513/jis.v22i2.8680.
X. He, “Sentiment Classification of Social Media User Comments Using SVM Models,” in 2024 5th International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT), Mar. 2024, pp. 1755–1759. doi: 10.1109/AINIT61980.2024.10581547.
S. Kumari, and M. P. Singh, “Machine Learning-Based Election Results Prediction Using Twitter Activity,” SN COMPUT. SCI., vol. 5, no. 7, p. 819, Aug. 2024. doi: 10.1007/s42979-024-03180-x.
V. Adarsh, V. Mohla, R. K. Mahto, and R. P, “Sentiment Classification of Product Reviews using Machine Learning,” in 2024 International Conference on Recent Advances in Electrical, Electronics, Ubiquitous Communication, and Computational Intelligence (RAEEUCCI), Apr. 2024, pp. 1–6. doi: 10.1109/RAEEUCCI61380.2024.10547858.
K. M. K. Kumar, K. Ullas, V. S. Reddy, M. S. Snehitha, M. E. Malkhed, and K. D. Kumar, “Detection of Bullying Text: A Multi-faceted Approach using Machine Learning and Natural Language Processing,” in 2024 5th International Conference on Smart Electronics and Communication (ICOSEC), Sep. 2024, pp. 180–186. doi: 10.1109/ICOSEC61587.2024.10722479.
K. Machova, M. Mikula, X. Gao, and M. Mach, “Lexicon-based Sentiment Analysis Using the Particle Swarm Optimization,” Electronics, vol. 9, no. 8, p. 1317, Aug. 2020. doi: 10.3390/electronics9081317.
O. Manullang, C. Prianto, and N. H. Harani, “ANALISIS SENTIMEN UNTUK MEMPREDIKSI HASIL CALON PEMILU PRESIDEN MENGGUNAKAN LEXICON BASED DAN RANDOM FOREST,” J. Ilm. Inform., vol. 11, no. 02, pp. 159–169, Sep. 2023. doi: 10.33884/jif.v11i02.7987.
A. Pulver, and S. Lyu, “LSTM with working memory,” in 2017 International Joint Conference on Neural Networks (IJCNN), Anchorage, AK, USA: IEEE, May 2017, pp. 845–851. doi: 10.1109/IJCNN.2017.7965940.
M. Khder, “Web Scraping or Web Crawling: State of Art, Techniques, Approaches and Application,” Int. J. Adv. Soft Comput. Its Appl., vol. 13, no. 3, pp. 145–168, Dec. 2021. doi: 10.15849/IJASCA.211128.11.
R. Arboretti et al., “An Integrated Framework for Automated Web Scraping and Sentiment Analysis of Product Reviews,” Journal of Machine Intelligence and Data Science, vol. 5, 2024. doi: 10.11159/jmids.2024.006.
J. S. Saltz, “CRISP-DM for Data Science: Strengths, Weaknesses and Potential Next Steps,” in 2021 IEEE International Conference on Big Data (Big Data), Dec. 2021, pp. 2337–2344. doi: 10.1109/BigData52589.2021.9671634.
A. M. Shimaoka, R. C. Ferreira, and A. Goldman, “The evolution of CRISP-DM for Data Science: Methods, Processes and Frameworks,” SBC Reviews on Computer Science, vol. 4, no. 1, Art. no. 1, Oct. 2024. doi: 10.5753/reviews.2024.3757.
L. Hickman, S. Thapa, L. Tay, M. Cao, and P. Srinivasan, “Text Preprocessing for Text Mining in Organizational Research: Review and Recommendations,” Organizational Research Methods, vol. 25, no. 1, pp. 114–146, Jan. 2022. doi: 10.1177/1094428120971683.
S. Feuerriegel et al., “Using natural language processing to analyse text data in behavioural science,” Nat Rev Psychol, vol. 4, no. 2, pp. 96–111, Feb. 2025. doi: 10.1038/s44159-024-00392-z.
N. Mughal, G. Mujtaba, S. Shaikh, A. Kumar, and S. M. Daudpota, “Comparative Analysis of Deep Natural Networks and Large Language Models for Aspect-Based Sentiment Analysis,” IEEE Access, vol. 12, pp. 60943–60959, 2024. doi: 10.1109/ACCESS.2024.3386969.
A. K. Zarandi and S. Mirzaei, “A survey of aspect-based sentiment analysis classification with a focus on graph neural network methods,” Multimed Tools Appl, vol. 83, no. 19, pp. 56619–56695, Jun. 2024. doi: 10.1007/s11042-023-17701-y.