Enhancing Support Vector Machine Performance Using Particle Swarm Optimization for Sentiment Analysis

Christofer Satria, Anthony Anggrawan, Peter Wijaya Sugijanto, Husain Husain, I Nyoman Yoga Sumadewa, Victoria Cynthia Rebecca

Abstract


Recently, social media has established itself as a leading platform in various sectors. Meanwhile, text extraction and classification in sentiment analysis have garnered significant attention in research. Regrettably, traditional sentiment analysis frequently falls short of accurately capturing the nuances of sentiment. At the same time, machine learning has enabled more effective sentiment analysis in data mining, classification, and the development of models that incorporate artificial intelligence. Therefore, the purpose of this study is to optimize sentiment analysis of public opinion in social media regarding Grand Prix motorcycle racing (MotoGP) and World Superbike (WSBK) events using machine learning and an optimized machine learning method. This study applies the Support Vector Machine (SVM) machine learning method and enhances its performance through optimization by integrating it with the Particle Swarm Optimization (PSO) algorithm. This study found that the SVM method produced accuracy, recall, and F1-score values of 80.15%, 75.63%, and 76.89%, respectively. In contrast, the SVM method, combined with PSO, achieves accuracy, recall, and F1-score rates of 81.82%, 79.9%, and 79.62%, respectively, in classifying the sentiment of events occurring in sporting events. The implications suggest that the application of Hybrid SVM with PSO significantly enhances classification accuracy in sentiment analysis.

Keywords


Sentiment analysis; SVM; Particle swarm; Social media; Machine learning; Data mining

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References


M. Wankhade, A. C. S. Rao, and C. Kulkarni, A survey on sentiment analysis methods, applications, and challenges, vol. 55, no. 7. Springer Netherlands, 2022. doi: 10.1007/s10462-022-10144-1.

M. Z. Asghar, A. Khan, A. Bibi, F. M. Kundi, and H. Ahmad, “Sentence-Level Emotion Detection Framework Using Rule-Based Classification,” Cognit. Comput., vol. 9, no. 6, pp. 868–894, 2017, doi: 10.1007/s12559-017-9503-3.

A. S. Talaat, “Sentiment analysis classification system using hybrid BERT models,” J. Big Data, vol. 10, no. 1, pp. 1–18, 2023, doi: 10.1186/s40537-023-00781-w.

M. Água, N. António, P. Carrasco, and C. Rassal, “Large Language Models Powered Aspect-Based Sentiment Analysis for Enhanced Customer Insights,” Tour. Manag. Stud., vol. 21, no. 1, pp. 1–19, 2025, doi: 10.18089/tms.202501011.

O. Alsemaree, A. S. Alam, S. S. Gill, and S. Uhlig, “Sentiment analysis of Arabic social media texts: A machine learning approach to deciphering customer perceptions,” Heliyon, vol. 10, no. 9, p. e27863, 2024, doi: 10.1016/j.heliyon.2024.e27863.

A. Chourasiya, A. Khan, K. Bajaj, M. Tomar, T. Kohli, and D. Chauhan, “A Review of Sentiment Analysis and Emotion Detection from Text using Different Models,” no. January, 2025.

X. Liu, R. Li, S. Ye, G. Zhang, and X. Wang, “Multimodal Aspect-Based Sentiment Analysis under Conditional Relation,” in Proceedings - International Conference on Computational Linguistics, COLING, 2025, pp. 313–323.

Z. Zhao and S. Yang, “Research on Red Cultural Inheritance and Application of SVM Support Vector,” Appl. Math. Nonlinear Sci., vol. 10, no. 1, pp. 1–18, 2025.

M. A. J. Eljatin, R. W. S. Sumadinata, and D. S. Sari, “Sports and Cultural Diplomacy Integration in Post-Pandemic COVID-19 International Events: Evidence from Indonesia’s MotoGP Mandalika 2022,” Daengku J. Humanit. Soc. Sci. Innov., vol. 5, no. 1, pp. 113–120, 2025, doi: 10.35877/454ri.daengku3745.

H. Q. Low, P. Keikhosrokiani, and M. Pourya Asl, “Decoding violence against women: analysing harassment in middle eastern literature with machine learning and sentiment analysis,” Humanit. Soc. Sci. Commun., vol. 11, no. 1, pp. 1–18, 2024, doi: 10.1057/s41599-024-02908-7.

N. A. Semary, W. Ahmed, K. Amin, P. Pławiak, and M. Hammad, “Enhancing machine learning-based sentiment analysis through feature extraction techniques,” PLoS One, vol. 19, no. 2 February, pp. 1–12, 2024, doi: 10.1371/journal.pone.0294968.

A. Anggrawan, P. W. Sugijanto, C. Satria, A. D. Dayani, H. Wardhana, and A. S. Abdi, “Comparison of Sentiment Analysis Evaluation Regarding International Mobile Equipment Identity Blocking between the K-Nearest Neighbor and Support Vector Machine Methods,” in 2024 5th International Conference on Computational Science & Information Management (ICoCSIM), 2024, pp. 193–199. doi: 10.1109/ICoCSIM65098.2024.00003.

V. Echambadi and I. High, “Financial Market Sentiment Analysis Using LLM and RAG,” J. SSRN, pp. 1–7, 2025.

A. Anggrawan, C. Satria, C. K. Nuraini, Lusiana, N. G. A. Dasriani, and Mayadi, “Machine Learning for Diagnosing Drug Users and Types of Drugs Used,” Int. J. Adv. Comput. Sci. Appl., vol. 12, no. 11, pp. 111–118, 2021.

A. Anggrawan, Mayadi, C. Satria, B. K. Triwijoyo, and R. Rismayati, “Comparative Analysis of Machine Learning in Predicting the Treatment Status of COVID-19 Patients,” J. Adv. Inf. Technol., vol. 14, no. 1, pp. 56–65, 2023, doi: 10.12720/jait.14.1.56-65.

P. Guleria, S. Ahmed, A. Alhumam, and P. N. Srinivasu, “Empirical Study on Classifiers for Earlier Prediction of COVID-19 Infection Cure and Death Rate in the Indian States,” Healthc., vol. 10, no. 1, 2022, doi: 10.3390/healthcare10010085.

A. Alqarni, “A Support Vector Machine (SVM) Model for Privacy Recommending Data Processing Model (PRDPM) in Internet of Vehicles,” Comput. Mater. Contin., vol. 82, no. 1, pp. 389–406, 2024, doi: 10.48084/etasr.7743.

T. Kavzoglu, F. Bilucan, and A. Teke, “Comparison of support vector machines, random forest and decision tree methods for classification of sentinel - 2A image using different band combinations,” ACRS 2020 - 41st Asian Conf. Remote Sens., no. November, pp. 1–9, 2020.

M. Sheykhmousa, M. Mahdianpari, H. Ghanbari, F. Mohammadimanesh, P. Ghamisi, and S. Homayouni, “Support Vector Machine Versus Random Forest for Remote Sensing Image Classification: A Meta-Analysis and Systematic Review,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 13, pp. 6308–6325, 2020, doi: 10.1109/JSTARS.2020.3026724.

S. Xu, H. Wu, J. Luo, J. Chen, and H. Jia, “The Application of Support Vector Machine (SVM) in Industrial Carbon Accounting Prediction and Green Electricity Control Strategies,” in E3S Web of Conferences, 2025, pp. 1–8. doi: 10.1051/e3sconf/202561501012.

P. Crespo Sogas, I. Fuentes Molina, À. Araujo Batlle, and J. M. Raya Vílchez, “Economic and social yield of investing in a sporting event: Sustainable value creation in a territory,” Sustain., vol. 13, no. 13, pp. 1–15, 2021, doi: 10.3390/su13137033.

D. Parra-Camacho, M. H. González-Serrano, M. Alguacil Jiménez, and P. Jiménez-Jiménez, “Analysis of the contribution of sport events to sustainable development: Impacts, support and resident’s perception,” Heliyon, vol. 9, no. 11, pp. 1–10, 2023, doi: 10.1016/j.heliyon.2023.e22033.

S. Owen and D. Chambers, “Volunteers’ Sense of (Dis)Connection at a Sport Event,” Leis. Sci., vol. 46, no. 2, pp. 105–122, 2024, doi: 10.1080/01490400.2021.1916660.

K. Kogoya, T. S. Guntoro, and M. F. P. Putra, “Sports Event Image, Satisfaction, Motivation, Stadium Atmosphere, Environment, and Perception: A Study on the Biggest Multi-Sport Event in Indonesia during the Pandemic,” Soc. Sci., vol. 11, no. 6, pp. 1–39, 2022, doi: 10.3390/socsci11060241.

T. M. Shami, A. A. El-Saleh, M. Alswaitti, Q. Al-Tashi, M. A. Summakieh, and S. Mirjalili, “Particle Swarm Optimization: A Comprehensive Survey,” IEEE Access, vol. 10, pp. 10031–10061, 2022, doi: 10.1109/ACCESS.2022.3142859.

E. H. Houssein, A. G. Gad, K. Hussain, and P. N. Suganthan, “Major Advances in Particle Swarm Optimization: Theory, Analysis, and Application,” Swarm Evol. Comput., vol. 63, no. February, p. 100868, 2021, doi: 10.1016/j.swevo.2021.100868.

M. Bordoloi and S. K. Biswas, Sentiment analysis: A survey on design framework, applications and future scopes, vol. 56, no. 11. Springer Netherlands, 2023. doi: 10.1007/s10462-023-10442-2.

M. Rodríguez-Ibánez, A. Casánez-Ventura, F. Castejón-Mateos, and P. M. Cuenca-Jiménez, “A review on sentiment analysis from social media platforms,” Expert Syst. Appl., vol. 223, no. March, 2023, doi: 10.1016/j.eswa.2023.119862.

O. Abiola, A. Abayomi-Alli, O. A. Tale, S. Misra, and O. Abayomi-Alli, “Sentiment analysis of COVID-19 tweets from selected hashtags in Nigeria using VADER and Text Blob analyser,” J. Electr. Syst. Inf. Technol., vol. 10, no. 1, 2023, doi: 10.1186/s43067-023-00070-9.

K. Denecke and D. Reichenpfader, “Sentiment analysis of clinical narratives: A scoping review,” J. Biomed. Inform., vol. 140, no. March, pp. 1–13, 2023, doi: 10.1016/j.jbi.2023.104336.

Y. A. Singgalen, “Sentiment and Toxicity Analysis of Sport Event MotoGP Mandalika Circuit Using Cross-Industry Standard Process for Data-Mining,” J. Inf. Syst. Res., vol. 5, no. 3, pp. 731–741, 2024, doi: 10.47065/josh.v5i3.5056.

T. I. Pribadi, F. Fahry, M. Muharis, and E. D. P. Marswandi, “Analysis of Tourist Sentiment towards Tourist Attractions in the Mandalika Special Economic Zone Using the Naïve Bayes Method,” J. Bumigora Inf. Technol., vol. 6, no. 1, pp. 105–114, 2024, doi: 10.30812/bite.v6i1.4081.

D. Lamani et al., “SVM directed machine learning classifier for human action recognition network,” Sci. Rep., vol. 15, no. 1, pp. 1–18, 2025, doi: 10.1038/s41598-024-83529-7.

C. A. Nurhaliza Agustina, R. Novita, Mustakim, and N. E. Rozanda, “The Implementation of TF-IDF and Word2Vec on Booster Vaccine Sentiment Analysis Using Support Vector Machine Algorithm,” Procedia Comput. Sci., vol. 234, pp. 156–163, 2024, doi: 10.1016/j.procs.2024.02.162.

V. Nurcahyawati, Z. Mustaffa, and M. Khalaf, “Exceeding Manual Labeling: VADER Lexicon as an Accurate Alternative to Automatic Sentiment Classification,” Int. Arab J. Inf. Technol., vol. 22, no. 2, pp. 225–235, 2025, doi: 10.34028/iajit/22/2/2.

K. Barik and S. Misra, “Analysis of customer reviews with an improved VADER lexicon classifier,” J. Big Data, vol. 11, no. 1, pp. 1–19, 2024, doi: 10.1186/s40537-023-00861-x.

A. Mahmoudi, D. Jemielniak, and L. Ciechanowski, “Assessing Accuracy: A Study of Lexicon and Rule-Based Packages in R and Python for Sentiment Analysis,” IEEE Access, vol. 12, pp. 20169–20180, 2024, doi: 10.1109/ACCESS.2024.3353692.

N. W. S. Saraswati, I. K. G. D. Putra, M. Sudarma, and I. M. Sukarsa, “Enhance sentiment analysis in big data tourism using hybrid lexicon and active learning support vector machine,” Bull. Electr. Eng. Informatics, vol. 13, no. 5, pp. 3663–3674, 2024, doi: 10.11591/eei.v13i5.7807.

T. Ahmed Khan, R. Sadiq, Z. Shahid, M. M. Alam, and M. Mohd Su’ud, “Sentiment Analysis using Support Vector Machine and Random Forest,” J. Informatics Web Eng., vol. 3, no. 1, pp. 67–75, 2024, doi: 10.33093/jiwe.2024.3.1.5.

Y. Li, J. Zhou, F. Chen, and M. Sun, “An improved particle swarm optimization for wind resistance performance design of high-rise buildings,” Adv. Wind Eng., vol. 2, no. November 2024, pp. 1–12, 2025, doi: 10.1016/c2009-0-09723-2.




DOI: http://doi.org/10.11591/ijict.v15i2.pp523-534

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