Sentiment Analysis Regarding Candidate Presidential 2024 Using Support Vector Machine Backpropagation Based

Authors

  • Atmaja Jalu Narendra Kisma Department Informatics, Amikom Purwokerto University http://orcid.org/0000-0002-1231-9970
  • Primandani Arsi Department Informatics, Amikom Purwokerto University
  • Pungkas Subarkah Department Informatics, Amikom Purwokerto University

DOI:

https://doi.org/10.31764/jtam.v8i1.17294

Keywords:

Sentiment Analysis, Support Vector Machine, Backpropagation, Presidential Candidates.

Abstract

This research has the potential to make an important contribution to the development of computationally-based sentiment analysis, particularly in the political context. Anies Baswedan, Ganjar Pranowo, and Prabowo Subianto, three candidates for the presidency of Indonesia, are examined using a Backpropagation-based Support Vector Machine (SVM) methodology in this study. This approach is used to categorize emotions into three groups: neutral, adverse, and favorable. Between July 1 and July 30, 2023, data on tweets mentioning the three presidential contenders was gathered. After processing the data, SVM was used while lowering the backpropagation process. The study's findings demonstrate that the performance of the model in determining public sentiment is greatly enhanced by the application of backpropagation-based SVM techniques. For each presidential contender, the evaluation was conducted using the f1 score, recall, and precision metrics. The evaluation's findings indicate that while the model struggles to distinguish between favorable and negative feelings toward particular presidential contenders, it performs better when categorizing neutral feelings. The SVM model is more accurately able to identify popular sentiment toward the three presidential candidates when the backpropagation approach is used. The results of the sentiment analysis are also represented by word clouds for each presidential contender, giving an intuitive sense of the words that are frequently used in public discourse. This study sheds light on the possibilities of using Twitter data to analyze political sentiment using the backpropagation-based SVM algorithm.

 

Author Biography

Atmaja Jalu Narendra Kisma, Department Informatics, Amikom Purwokerto University

Atmaja, lahir di Tegal, 9 Mei 2001. Mahasiswa Informatika Universitas Amikom Purwokerto.
Seorang Ketua Forum Asisten 2022. Penulis dapat dihubungi pada alamat email: atmajajnk69@gmail.com

References

Achmad, R. R., & Haris, M. (2023). Hyperparameter Tuning Deep Learning for Imbalanced Data. Tepian, 4(2), 90–101. https://doi.org/10.51967/tepian.v4i2.2216

Ahmad, M., Aftab, S., & Ali, I. (2017). Sentiment Analysis of Tweets using SVM. International Journal of Computer Applications, 177(5), 25–29. https://doi.org/10.5120/ijca2017915758

Amani, M., Ghorbanian, A., Ahmadi, S. A., Kakooei, M., Moghimi, A., Mirmazloumi, S. M., Moghaddam, S. H. A., Mahdavi, S., Ghahremanloo, M., Parsian, S., Wu, Q., & Brisco, B. (2020). Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 5326–5350. https://doi.org/10.1109/JSTARS.2020.3021052

Budiharto, W., & Meiliana, M. (2018). Prediction and analysis of Indonesia Presidential election from Twitter using sentiment analysis. Journal of Big Data, 5(1), 1–10. https://doi.org/10.1186/s40537-018-0164-1

Chandar, S., Sankar, C., Vorontsov, E., Kahou, S. E., & Bengio, Y. (2019). Towards non-saturating recurrent units for modelling long-term dependencies. 33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Innovative Applications of Artificial Intelligence Conference, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019, 3280–3287. https://doi.org/10.1609/aaai.v33i01.33013280

Chen, W., Pourghasemi, H. R., Panahi, M., Kornejady, A., Wang, J., Xie, X., & Cao, S. (2017). Spatial prediction of landslide susceptibility using an adaptive neuro-fuzzy inference system combined with frequency ratio, generalized additive model, and support vector machine techniques. Geomorphology, 297, 69–85. https://doi.org/10.1016/j.geomorph.2017.09.007

Curato, N. (2017). Flirting with Authoritarian Fantasies? Rodrigo Duterte and the New Terms of Philippine Populism. Journal of Contemporary Asia, 47(1), 142–153. https://doi.org/10.1080/00472336.2016.1239751

Dwinarko, D., Sjafrizal, T., Muhamad, P., & Akbar, M. R. (2023). Actors Distortion of News Agencies Framing Surveys in Online Mass Media about Political Parties Bearer Presidential-Candidates 2024. Journal of Social Science, 4(1), 195–214. https://doi.org/10.46799/jss.v4i1.507

Elgeldawi, E., Sayed, A., Galal, A. R., & Zaki, A. M. (2021). Hyperparameter tuning for machine learning algorithms used for arabic sentiment analysis. Informatics, 8(4), 1–21. https://doi.org/10.3390/informatics8040079

Fatimathuzahra, A. A., Chaerani, D., & Firdaniza, F. (2022). Robust Optimization Model for Twitter Sentiment Analysis of PeduliLindungi Application. JTAM (Jurnal Teori Dan Aplikasi Matematika), 6(3), 744. https://doi.org/10.31764/jtam.v6i3.8624

Ghiassi, M., & Lee, S. (2018). A domain transferable lexicon set for Twitter sentiment analysis using a supervised machine learning approach. Expert Systems with Applications, 106, 197–216. https://doi.org/10.1016/j.eswa.2018.04.006

Han, K. X., Chien, W., Chiu, C. C., & Cheng, Y. T. (2020). Application of support vector machine (SVM) in the sentiment analysis of twitter dataset. Applied Sciences (Switzerland), 10(3). https://doi.org/10.3390/app10031125

Karami, A., Bennett, L. S., & He, X. (2018). Mining Public Opinion about Economic Issues. International Journal of Strategic Decision Sciences, 9(1), 18–28. https://doi.org/10.4018/ijsds.2018010102

Kursuncu, U., Gaur, M., Lokala, U., Thirunarayan, K., Sheth, A., & Arpinar, I. B. (2019). Predictive analysis on Twitter: Techniques and applications. Emerging research challenges and opportunities in computational social network analysis and mining, 67-104. https://doi.org/10.1007/978-3-319-94105-9_4

Li, A., & Chen, Y. (2017). Pre-proceΒing analysis for Chinese text sentiment analysis. ACM International Conference Proceeding Series, 318–323. https://doi.org/10.1145/3158233.3159317

Liu, Z. H., Lu, B. L., Wei, H. L., Chen, L., Li, X. H., & Ratsch, M. (2021). Deep adversarial domain adaptation model for bearing fault diagnosis. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 51(7), 4217–4226. https://doi.org/10.1109/TSMC.2019.2932000

Manek, A. S., Shenoy, P. D., Mohan, M. C., & Venugopal, K. R. (2017). Aspect term extraction for sentiment analysis in large movie reviews using Gini Index feature selection method and SVM classifier. World Wide Web, 20(2), 135–154. https://doi.org/10.1007/s11280-015-0381-x

Medhat, W., Hassan, A., & Korashy, H. (2014). Sentiment analysis algorithms and applications: A survey. Ain Shams Engineering Journal, 5(4), 1093–1113. https://doi.org/10.1016/j.asej.2014.04.011

Oliveira, D. J. S., Bermejo, P. H. de S., & dos Santos, P. A. (2017). Can social media reveal the preferences of voters? A comparison between sentiment analysis and traditional opinion polls. Journal of Information Technology and Politics, 14(1), 34–45. https://doi.org/10.1080/19331681.2016.1214094

Rennó, L. R. (2020). The Bolsonaro Voter: Issue Positions and Vote Choice in the 2018 Brazilian Presidential Elections. Latin American Politics and Society, 62(4), 1–23. https://doi.org/10.1017/lap.2020.13

Rintyarna, B. S. (2021). Mapping acceptance of indonesian organic food consumption under COVID-19 pandemic using sentiment analysis of Twitter dataset. Journal of Theoretical and Applied Information Technology, 99(5), 1009–1019.

Shofiya, C., & Abidi, S. (2021). Sentiment analysis on covid-19-related social distancing in Canada using twitter data. International Journal of Environmental Research and Public Health, 18(11). https://doi.org/10.3390/ijerph18115993

Siregar, S. P., & Wanto, A. (2017). Analysis of Artificial Neural Network Accuracy Using Backpropagation Algorithm In Predicting Process (Forecasting). IJISTECH (International Journal Of Information System & Technology), 1(1), 34. https://doi.org/10.30645/ijistech.v1i1.4

Sugiyarto, S., Eliyanto, J., Irsalinda, N., Putri, Z., & Fitrianawat, M. (2021). A Fuzzy Logic in Election Sentiment Analysis: Comparison Between Fuzzy Naïve Bayes and Fuzzy Sentiment using CNN. JTAM (Jurnal Teori Dan Aplikasi Matematika), 5(1), 110. https://doi.org/10.31764/jtam.v5i1.3766

Wang, F., Zhen, Z., Mi, Z., Sun, H., Su, S., & Yang, G. (2015). Solar irradiance feature extraction and support vector machines based weather status pattern recognition model for short-term photovoltaic power forecasting. Energy and Buildings, 86, 427–438. https://doi.org/10.1016/j.enbuild.2014.10.002

Wanto, A., Windarto, A. P., Hartama, D., & Parlina, I. (2017). Use of Binary Sigmoid Function And Linear Identity In Artificial Neural Networks For Forecasting Population Density. IJISTECH (International Journal Of Information System & Technology), 1(1), 43. https://doi.org/10.30645/ijistech.v1i1.6

Wiyono, W., Qodir, Z., & Lestari, L. (2023). Online Media Trends on Political Party Sentiment Ahead of the 2024 Election in Indonesia. Journal of Governance, 8(1). https://doi.org/10.31506/jog.v8i1.17880

Yang, S., Zhu, F., Ling, X., Liu, Q., & Zhao, P. (2021). Intelligent Health Care: Applications of Deep Learning in Computational Medicine. Frontiers in Genetics, 12(April), 1–21. https://doi.org/10.3389/fgene.2021.607471

Zhou, C., Sun, C., Liu, Z., & Lau, F. C. M. (2015). A C-LSTM Neural Network for Text Classification. http://arxiv.org/abs/1511.08630

Downloads

Published

2024-01-19

Issue

Vol. 8 No. 1 (2024): January

Section

Articles

License

Authors who publish articles in JTAM (Jurnal Teori dan Aplikasi Matematika) agree to the following terms:

  1. Authors retain copyright of the article and grant the journal right of first publication with the work simultaneously licensed under a CC-BY-SA or The Creative Commons Attribution–ShareAlike License.
  2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
  3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).