The Role of Sentiment Analysis in Election Predictions Compared to Electability Surveys
DOI:
https://doi.org/10.64021/ijmst.1.1.1-8.2025Keywords:
electability, election, sentiment, president, svmAbstract
Indonesia has just held the voting process for the Presidential Election. This has become a discussion of various media to social media, especially Twitter. However, when making predictions based on social media it will be so difficult if there is no specific technique or method for handling it. The prediction method we found in Indonesia often uses electability surveys in elections, but this research will compare it with sentiment analysis that utilizes social media in data collection. Another novelty is the data used during candidate campaign debates using the Support Vector Machine (SVM) method in class classification. The results obtained show that there are still differences between electability and sentiment, but this is due to several factors such as the amount of data, data objects, data collection time span, and methods. Overall, the SVM method has an accuracy of more than 0.75 on all three candidate datasets, proving that this method can be applied to similar cases.
References
J. P. Darr and R. L. Stiles, “Should Campaigns Respond to Electability Arguments?,” J. Polit. Mark., vol. 21, no. 1, pp. 41–55, Jan. 2022, doi: 10.1080/15377857.2018.1538923.
H. Yas et al., “The Negative Role of Social Media During the COVID-19 Outbreak,” Int. J. Sustain. Dev. Plan.,vol. 16, no. 2, pp. 219–228, Apr. 2021, doi: 10.18280/ijsdp.160202.
B. Alsari et al., “Sentiment analysis for cruises in Saudi Arabia on social media platforms using machine learning algorithms,” J. Big Data, vol. 9, p. 21, Dec. 2022, doi: 10.1186/s40537-022-00568-5.
A. Zahri, R. Adam, and E. B. Setiawan, “Social Media Sentiment Analysis using Convolutional Neural Network (CNN) dan Gated Recurrent Unit (GRU),” J. Ilm. Tek. Elektro Komput. dan Inform., vol. 9, no. 1, pp. 119–131, 2023, doi: 10.26555/jiteki.v9i1.25813.
E. S. Prihatini, “Women and social media during legislative elections in Indonesia,” Womens. Stud. Int. Forum, vol. 83, p. 102417, Nov. 2020, doi: 10.1016/j.wsif.2020.102417.
S. Hinduja, M. Afrin, S. Mistry, and A. Krishna, “Machine learning-based proactive social-sensor service for mental health monitoring using twitter data,” Int. J. Inf. Manag. Data Insights, vol. 2, p. 100113, 2022, doi: 10.1016/j.jjimei.2022.100113.
N. M. Azahra and E. B. Setiawan, “Sentence-Level Granularity Oriented Sentiment Analysis of Social Media Using Long Short-Term Memory (LSTM) and IndoBERTweet Method,” J. Ilm. Tek. Elektro Komput. dan Inform., vol. 9, no. 1, pp. 85–95, 2023, doi: 10.26555/jiteki.v9i1.25765.
B. Haryanto, Y. Ruldeviyani, F. Rohman, J. Dimas, R. Magdalena, and M. Yasil, “Facebook analysis of community sentiment on 2019 Indonesian presidential candidates from Facebook opinion data,” Procedia Comput. Sci., vol.161, pp. 715–722, 2019, doi: 10.1016/j.procs.2019.11.175.
M. Yousef and A. ALali, “Analysis and Evaluation of Two Feature Selection Algorithms in Improving the Performance of the Sentiment Analysis Model of Arabic Tweets,” Int. J. Adv. Comput. Sci. Appl., vol. 13, no. 6, pp. 705–711, 2022, doi: 10.14569/IJACSA.2022.0130683.
A. M. Iddrisu, S. Mensah, F. Boafo, G. R. Yeluripati, and P. Kudjo, “A sentiment analysis framework to classify instances of sarcastic sentiments within the aviation sector,” Int. J. Inf. Manag. Data Insights, vol. 3, p. 100180, 2023, doi: 10.1016/j.jjimei.2023.100180.
Z. A. Diekson, M. R. B. Prakoso, M. S. Q. Putra, M. S. A. F. Syaputra, S. Achmad, and R. Sutoyo, “Sentiment analysis for customer review: Case study of Traveloka,” Procedia Comput. Sci., vol. 216, pp. 682–690, 2023, doi: 10.1016/j.procs.2022.12.184.
O. A. Czeranowska et al., “Migrants vs. Stayers in the Pandemic – A Sentiment Analysis of Twitter Content,” SSRN Electron. J., vol. 10, no. March, 2023, doi: 10.2139/ssrn.4358023.
R. Zeitun, M. U. Rehman, N. Ahmad, and X. V. Vo, “The impact of Twitter-based sentiment on US sectoral returns,” North Am. J. Econ. Financ., vol. 64, no. June 2022, p. 101847, 2023, doi: 10.1016/j.najef.2022.101847.
R. Catelli, S. Pelosi, C. Comito, C. Pizzuti, and M. Esposito, “Lexicon-based sentiment analysis to detect opinions and attitude towards COVID-19 vaccines on Twitter in Italy,” Comput. Biol. Med., vol. 158, no. February, p.106876, 2023, doi: 10.1016/j.compbiomed.2023.106876.
L. Samaras, E. García-barriocanal, and M. Sicilia, “Sentiment analysis of COVID-19 cases in Greece using Twitter data,” Expert Syst. Appl., vol. 230, no. May, p. 120577, 2023, doi: 10.1016/j.eswa.2023.120577.
A. Ammari, K. Chebbi, and N. Ben Arfa, “How does the COVID-19 pandemic shape the relationship between Twitter sentiment and stock liquidity of US firms?,” Int. Rev. Financ. Anal., vol. 88, no. January, p. 102633, 2023, doi: 10.1016/j.irfa.2023.102633.
D. Antypas, A. Preece, and J. Camacho-Collados, “Negativity spreads faster: A large-scale multilingual twitter analysis on the role of sentiment in political communication,” Online Soc. Networks Media, vol. 33, no. January, 2023, doi: 10.1016/j.osnem.2023.100242.
A. Karami et al., “2020 U.S. presidential election in swing states: Gender differences in Twitter conversations,” Int. J. Inf. Manag. Data Insights, vol. 2, p. 100097, 2022, doi: 10.1016/j.jjimei.2022.100097.
D. O. Oyewola, L. A. Oladimeji, S. O. Julius, L. B. Kachalla, and E. G. Dada, “Optimizing sentiment analysis of Nigerian 2023 presidential election using two-stage residual long short term memory,” Heliyon, vol. 9, no. 4, p.e14836, 2023, doi: 10.1016/j.heliyon.2023.e14836.
O. Olabanjo et al., “From Twitter to Aso-Rock: A Natural Language Processing Spotlight for Understanding Nigeria 2023 Presidential Election,” Heliyon, vol. 9, no. 5, p. e16085, 2022, doi: 10.1016/j.heliyon.2023.e16085.
A. Sharma and U. Ghose, “Sentimental Analysis of Twitter Data with respect to General Elections in India,” Procedia Comput. Sci., vol. 173, no. 2019, pp. 325–334, 2020, doi: 10.1016/j.procs.2020.06.038.
K. Brito and P. J. L. Adeodato, “Machine learning for predicting elections in Latin America based on social media engagement and polls,” Gov. Inf. Q., vol. 40, no. 1, p. 101782, Jan. 2023, doi: 10.1016/j.giq.2022.101782.
K. Dos Santos Brito, S. R. De Lemos Meira, and P. J. L. Adeodato, “Correlations of social media performance and electoral results in Brazilian presidential elections,” Inf. Polity, vol. 26, no. 4, pp. 417–439, 2021, doi: 10.3233/IP-210315.
M. Chiny, M. Chihab, Y. Chihab, and O. Bencharef, “LSTM, VADER and TF-IDF based Hybrid Sentiment Analysis Model,” Int. J. Adv. Comput. Sci. Appl., vol. 12, no. 7, pp. 265–275, 2021, doi: 10.14569/IJACSA.2021.0120730.
A. A. Firdaus, A. Yudhana, I. Riadi, and Mahsun, “Indonesian presidential election sentiment: Dataset of response public before 2024,” Data Br., vol. 52, p. 109993, 2024, doi: 10.1016/j.dib.2023.109993.
R. Ahuja, A. Chug, S. Kohli, S. Gupta, and P. Ahuja, “The impact of features extraction on the sentiment analysis,” Procedia Comput. Sci., vol. 152, pp. 341–348, 2019, doi: 10.1016/j.procs.2019.05.008.
S. Cipolla and J. Gondzio, “Training very large scale nonlinear SVMs using Alternating Direction Method of Multipliers coupled with the Hierarchically Semi Separable kernel approximations,” EURO J. Comput. Optim., vol.10, 2022, doi: 10.1016/j.ejco.2022.100046.
S. Rani and S. Bhatt, “Sentiment Analysis on twitter data using Machine Learning,” J. Xidian Univ., vol. 14, no. 12, Dec. 2020, doi: 10.37896/jxu14.12/039.
S. Zahoor and R. Rohilla, “Twitter Sentiment Analysis Using Machine Learning Algorithms: A Case Study,” in 2020 International Conference on Advances in Computing, Communication & Materials (ICACCM), Aug. 2020, pp. 194–199. doi: 10.1109/ICACCM50413.2020.9213011.
A. Tariq et al., “Modelling, mapping and monitoring of forest cover changes, using support vector machine, kernel logistic regression and naive bayes tree models with optical remote sensing data,” Heliyon, vol. 9, no. 2, 2023, doi: 10.1016/j.heliyon.2023.e13212.
K. Kusrini, E. T. Luthfi, M. Muqorobin, and R. W. Abdullah, “Comparison of Naive Bayes and K-NN Method on Tuition Fee Payment Overdue Prediction,” in 2019 4th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE), Nov. 2019, pp. 125–130. doi:10.1109/ICITISEE48480.2019.9003782.
F. Ernawan, A. Aminuddin, D. Nincarean, M. F. A. Razak, and A. Firdaus, “Three Layer Authentications with a Spiral Block Mapping to Prove Authenticity in Medical Images,” Int. J. Adv. Comput. Sci. Appl., vol. 13, no. 4, 2022, doi: 10.14569/IJACSA.2022.0130425.
A. Aminuddin and F. Ernawan, “AuSR1: Authentication and self-recovery using a new image inpainting technique with LSB shifting in fragile image watermarking,” J. King Saud Univ. - Comput. Inf. Sci., vol. 34, no. 8, pp. 5822–5840, Sep. 2022, doi: 10.1016/j.jksuci.2022.02.009.
W. Liu, S. Liang, and X. Qin, “Weighted p-norm distance t kernel SVM classification algorithm based on improved polarization,” Sci. Rep., vol. 12, no. 1, pp. 1–17, 2022, doi: 10.1038/s41598-022-09766-w.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Indonesian Journal of Modern Science and Technology
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
