Sentiment Analysis On Twitter Posts About The Russia and Ukraine War With Long Short-Term Memory


  • Anthony Xu Universitas Prima Indonesi
  • Tiffany Universitas Prima Indonesi
  • Matthew Evan Phanie Universitas Prima Indonesi
  • Allwin Simarmata Universitas Prima Indonesi




Sentiment analysis is one method for evaluating public opinion from the received text.  In this study, we evaluate the performance of the LSTM model with Sastrawi in sentiment analysis in Indonesian using a Twitter dataset totaling 2537 data collected regarding the Russo-Ukrainian war.  The purpose of this study is to determine the reliability of the LSTM model with Sastrawi in sentiment analysis in Indonesian and to evaluate the performance of the model with the collected Twitter dataset regarding the Russian-Ukrainian war.  The method used in this study is data pre-processing, training and validation of the LSTM model with Literature, and model evaluation using the metrics of accuracy, precision, recall, and F1 score.  In the dataset collected in this study, positive, neutral and negative sentiments were 54.7%, 35% and 10.2%.  The results obtained from this study indicate that the LSTM model with Literature can provide good results in sentiment analysis with a prediction accuracy of 82%.  The implication of the results of this study is that the LSTM model with Sastrawi can be used for sentiment analysis on Twitter and further research needs to be carried out with a wider and more diverse dataset, especially to produce even better accuracy.

GS Cited Analysis


Download data is not yet available.


A. Shevtsov, C. Tzagkarakis, D. Antonakaki, P. Pratikakis, and S. Ioannidis, “Twitter Dataset on the Russo-Ukrainian War,” arXiv preprint arXiv:2204.08530, 2022.

G. Melnik, B. Misonzhnikov, and E. Vojtik, “The Image of Russia in the Western Press as a «Military Threat» Tool: Following the Media Content,” National Resilience, Politics and Society, vol. 1, no. 2, pp. 225–250, 2019.

B. Džubur, Ž. Trojer, and U. Zrimšek, “Semantic Analysis of Russo-Ukrainian War Tweet Networks,” 2022.

B. Chen et al., “Public Opinion Dynamics in Cyberspace on Russia-Ukraine War: A Case Analysis With Chinese Weibo,” IEEE Trans Comput Soc Syst, 2022.

M. Siino, E. di Nuovo, I. Tinnirello, and M. la Cascia, “Fake News Spreaders Detection: Sometimes Attention Is Not All You Need,” Information, vol. 13, no. 9, p. 426, 2022.

B. Siswanto and Y. Dani, “Sentiment Analysis about Oximeter as Covid-19 Detection Tools on Twitter Using Sastrawi Library,” in 2021 8th International Conference on Information Technology, Computer and Electrical Engineering (ICITACEE), 2021, pp. 161–164.

M. A. Rosid, A. S. Fitrani, I. R. I. Astutik, N. I. Mulloh, and H. A. Gozali, “Improving text preprocessing for student complaint document classification using sastrawi,” in IOP Conference Series: Materials Science and Engineering, 2020, vol. 874, no. 1, p. 012017.

S. Patil and V. Lokesha, “Live Twitter Sentiment Analysis Using Streamlit Framework,” Available at SSRN 4119949, 2022.

S. Rai, G. S B, and J. Kumar, “Sentiment Analysis of Twitter Data,” International Research Journal on Advanced Science Hub, vol. 2, pp. 56–61, 2020.

U. D. Gandhi, P. Malarvizhi Kumar, G. Chandra Babu, and G. Karthick, “Sentiment analysis on twitter data by using convolutional neural network (CNN) and long short term memory (LSTM),” Wirel Pers Commun, pp. 1–10, 2021.

Y. M. Wazery, H. S. Mohammed, and E. H. Houssein, “Twitter sentiment analysis using deep neural network,” in 2018 14th International Computer Engineering Conference (ICENCO), 2018, pp. 177–182.

K. Elshakankery and M. F. Ahmed, “HILATSA: A hybrid Incremental learning approach for Arabic tweets sentiment analysis,” Egyptian Informatics Journal, vol. 20, no. 3, pp. 163–171, 2019.

F. Koto and G. Y. Rahmaningtyas, “Inset lexicon: Evaluation of a word list for Indonesian sentiment analysis in microblogs,” in 2017 International Conference on Asian Language Processing (IALP), 2017, pp. 391–394.

J. L. Davis and T. P. Love, “Intersecting matters:# GeorgeFloyd and# COVID19,” First Monday, 2022.

E. Winarko, “Sentimen analisis tweet berbahasa Indonesia dengan deep belief network,” IJCCS (Indonesian Journal of Computing and Cybernetics Systems), vol. 11, no. 2, pp. 187–198, 2017.

I. Coombs, “Assessing the Effectiveness of Schools to Safeguard Their Pupil’s Use of Social Media Through an Analysis of School Inspection Reports,” The Buckingham Journal of Education, vol. 2, no. 2, 2021.


Crossmark Updates

How to Cite

Xu, A., Tiffany, Phanie, M. E., & Simarmata, A. (2023). Sentiment Analysis On Twitter Posts About The Russia and Ukraine War With Long Short-Term Memory. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 8(2), 789-797.