This study proposes the use of machine learning models, namely Support Vector Machine (SVM), for effective sentiment analysis on a dataset from the Kaggle repository. Considering the Tinubu 2023 election dataset, it can be seen that SVM having been fed with the cleansed dataset feature obtained an accuracy score of 93.2%, considering the result of each algorithm on the 2023 Nigerian election datasets. The study investigates data preprocessing techniques, feature selection, and correlation metrics to optimize the sentiment detection process. Results show that the SVM model achieves the highest accuracy, making it a potential tool for political analysis, business marketing, and public policy implementation. However, future research may explore deep learning techniques and data balancing strategies to enhance the models' performance further.

Keywords: Machine learning, Opinion mining, Sentiment analysis, Support vector machine, Natural language processing, Social media.

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, 10(3): 1125. https://doi.org/10.3390/app10031125.

Alyami, S.N., & Olatunji, S.O. (2020). Application of support vector machine for Arabic sentiment classification using twitter-based dataset. Journal of Information & Knowledge Management, 19(01): 2040018. 

Imanuddin, S.H., Adi, K., & Gernowo, R. (2023). Sentiment Analysis on Satusehat Application Using Support Vector Machine Method. Journal of Electronics, Electromedical Engineering, and Medical Informatics, 5(3): 143–149. https://doi.org/10.35882/jeemi.v5i3.304.

Saif, H., Fernandez, M., Kastler, L., & Alani, H. (2017). Sentiment lexicon adaptation with context and semantics for the social web. Semantic Web, 8(5): 643–665. https://doi.org/10.3233/sw-170265.

Dey, R.K., Sarddar, D., Sarkar, I., Bose, R., & Roy, S. (2020). A literature survey on sentiment analysis techniques involving social media and online platforms. International Journal of Scientific & Technology Research, 1(1): 166–173.

Birjali, M., Kasri, M., & Beni-Hssane, A. (2021). A comprehensive survey on sentiment analysis: Approaches, challenges and trends. Knowledge-Based Systems, 226: 107134.

Agaian, S., & Kolm, P. (2017). Financial sentiment analysis using machine learning techniques. International Journal of Investment Management and Financial Innovations, 3(1): 1–9.

Phan, H.T., Tran, V.C., Nguyen, N.T., & Hwang, D. (2020). Improving the performance of sentiment analysis of tweets containing fuzzy sentiment using the feature ensemble model. IEEE Access, 8: 14630–14641. https://doi. org/10.1109/access.2019.2963702.

Cachola, I., Holgate, E., Preoţiuc-Pietro, D., & Li, J.J. (2018). Expressively vulgar: The socio-dynamics of vulgarity and its effects on sentiment analysis in social media. In Proceedings of the 27th International Conference on Computational Linguistics, Pages 2927–2938.

Hasan, M., Rundensteiner, E., & Agu, E. (2019). Automatic emotion detection in text streams by analyzing twitter data. International Journal of Data Science and Analytics, 7: 35–51. https://doi.org/10.1007/s41060-018-0096-z.

Gupta, A., Tyagi, P., Choudhury, T., & Shamoon, M. (2019). Sentiment analysis using support vector machine. In 2019 International conference on contemporary computing and informatics (IC3I), Pages 49–53, IEEE. https://doi. org/10.1007/s41060-018-0096-z.

Nabillah, A., Alam, S., & Resmi, M.G. (2022). Twitter User Sentiment Analysis of TIX ID Applications Using Support Vector Machine Algorithm. RISTEC: Research in Information Systems and Technology, 3(1): 14–27.

Nursalim, A., & Novita, R. (2023). Sentiment Analysis of Comments on Google Play Store, Twitter and Youtube to the Mypertamina Application with Support Vector Machine. Jurnal Teknik Informatika (JUTIF), 4(6): 1305–1312.  https://doi.org/10.52436.

Ferdiansyah, H., Komaria, N., & Arief, I. (2023). The Application of Support Vector Machine Method to Analyze the Sentiments of Netizens on Social Media Regarding the Accessibility of Disabilities in Public Spaces. Journal of Information System, Technology and Engineering, 1(1): 6–10. https://doi.org/10.61487/jiste.v1i1.8.

Khan, T.A., Sadiq, R., Shahid, Z., Alam, M.M., & Mohd Su’ud, M.B. (2024). Sentiment Analysis using Support Vector Machine and Random Forest. Journal of Informatics and Web Engineering, 3(1): 67–75. https://doi.org/ 10.33093/jiwe.2024.3.1.5.

Gupta, S., Gaur, S.S., Sharmaa, P., & Gupta, A. (2024). Election Prediction Using Twitter Sentiment Analysis Using Naïve Bayes and Support Vector Machine. Available at SSRN 4754965. http://dx.doi.org/10.2139/ssrn. 4754965.

Firdaus, A.A., Yudhana, A., & Riadi, I. (2024). Prediction of Presidential Election Results using Sentiment Analysis with Pre and Post Candidate Registration Data. Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika, 10(1): 36–46. https://doi.org/10.23917/khif.v10i1.4836.

Dharta, F.Y., Mahardhani, A.J., Yahya, S.R., Dirsa, A., & Usulu, E.M. (2024). Application of Naive Bayes Classifier Method to Analyze Social Media User Sentiment towards the Presidential Election Phase. Jurnal Informasi dan Teknologi, Pages 176–181. https://doi.org/10.60083/jidt.v6i1.494.

Damayanti, L., & Lhaksmana, K.M. (2024). Sentiment analysis of the 2024 Indonesia presidential election on Twitter. Sinkron: jurnal dan penelitian teknik informatika, 8(2): 938–946. 

Wahyudi, A., Santoso, G.B., & Sholihah, B. (2024). Analysis of The Sentiment of Indonesian Presidential Candidates for 2024 on The YouTube Social Media Platform using The Support Vector Machine Method. Intelmatics, 4(1): 22–30. https://doi.org/10.25105/itm.v4i1.17636.

Fauzi, A., Butar, J.B., Budi, I., Ramadiah, A., Putra, P.K., & Santoso, A.B. (2024). Supervised Machine Learning Entity Sentiment Analysis: Prediction of Support for 2024 Indonesian Presidential Candidates. Revue d'Intelligence Artificielle, 38(2). https://doi.org/10.18280/ria.380222.

Khan, T.A., Sadiq, R., Shahid, Z., Alam, M.M., & Su'ud, M.B.M. (2024). Sentiment Analysis using Support Vector Machine and Random Forest. Journal of Informatics and Web Engineering, 3(1): 67–75. 

Source of Funding:

This study did not receive any grant from funding agencies in the public, commercial, or not–for–profit sectors.

Competing Interests Statement:

The authors declare no competing financial, professional, or personal interests.

Consent for publication:

The authors declare that they consented to the publication of this study.

Authors' contributions:

Both the authors made an equal contribution to the conception and design of the work, data collection, experimental analysis, writing of the article and critical revision of the article. Both the authors have read and approved the final copy of the manuscript.

Availability of data and material:

Authors are willing to share the data and materials according to relevant needs.