Sentiment Analysis and Topic Modelling on Twitter Related to Mobile Legends: Bang Bang Game Using Lexicon-Based, LDA, and SVM
Article Sidebar
Main Article Content
Abstract
Mobile Legends: Bang Bang (MLBB) has become a significant phenomenon within the global e-sports landscape, attracting millions of active players and fans. This study presents a comprehensive sentiment analysis and topic modelling of MLBB-related discussions on Platform Twitter, combining a lexicon-based approach, Latent Dirichlet Allocation (LDA), and Support Vector Machine (SVM) classification within a unified analytical pipeline. A dataset of 4,313 tweets was analysed, revealing that 70.8% expressed neutral sentiment, suggesting that much of the community's communication is informational rather than emotionally charged. Positive sentiments were associated with game content updates and rewards, while negative sentiments focused on technical and competitive issues. The SVM model achieved a sentiment classification accuracy of 90.57%, and cluster classification reached 85.13%. These findings offer valuable insights into how players engage with the game and reflect the underlying sentiments that influence the perception of gameplay and system updates. Furthermore, the predominance of neutral sentiment suggests opportunities for developers and content creators to enhance emotional resonance and community interaction through more engaging content and responsive design. The effectiveness of the combined methodology demonstrates the potential of integrating lexicon-based techniques with machine learning and topic modelling in analysing social media discourse within gaming communities. Future research is recommended to adopt advanced deep learning techniques, develop domain-specific sentiment lexicons, conduct multilingual sentiment analysis, and perform temporal tracking of community sentiment over time, enabling more dynamic and inclusive assessments of user experience and satisfaction.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
All articles published in JIWE are licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) License. Readers are allowed to
- Share — copy and redistribute the material in any medium or format under the following conditions:
- Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use;
- NonCommercial — You may not use the material for commercial purposes;
- NoDerivatives — If you remix, transform, or build upon the material, you may not distribute the modified material.
References
M. Yuzyk and P. Seidner, "E-Sports competitions," in Developments in Information & Knowledge Management for Business Applications: Volume 5, N. Kryvinska and M. Gregus, Eds., Cham: Springer International Publishing, 2022, pp. 671–716, doi: 10.1007/978-3-030-97008-6_30.
N. V. A. Cabiles, P. J. E. Samilo, Ma. S. Jane S. Magallanes, H. G. Adalia, and J. V. Chavez, "Qualitative exploration on the prevalence of swear words in MLBB among Filipino online players," Environmental and Social Psychology, vol. 10, no. 2, Feb. 2025, doi: 10.59429/esp.v10i2.3113.
W. Wang and J. and Fan, "Topic mining of real-time discussions: what catches the attention of live-streaming esports viewers?," European Sport Management Quarterly, vol. 24, no. 2, pp. 323–344, Mar. 2024, doi: 10.1080/16184742.2022.2117397.
S. R. Goniwada, "Sentiment analysis," in Introduction to Datafication: Implement Datafication Using AI and ML Algorithms, S. R. Goniwada, Ed., Berkeley, CA: Apress, 2023, pp. 165–184, doi: 10.1007/978-1-4842-9496-3_6.
P. Srinivas, K. Gayathri, K. Bhavitha, Jahnavi, and K. D. Sarath, "BLIP-NLP model for sentiment analysis," in 2023 2nd International Conference on Edge Computing and Applications (ICECAA), Jul. 2023, pp. 468–475, doi: 10.1109/ICECAA58104.2023.10212253.
L. Yuan, M. Ding, F. Meng, and Y. Tian, "Sentiment analysis and rating video game dimensions via NLP, " Applied and Computational Engineering, vol. 132, pp. 1–10, Jan. 2025, doi: 10.54254/2755-2721/2024.20540.
J. Watson et al., "Natural Language Processing to assess communication dynamics between cooperating dyads during video gameplay," in Neuroergonomics and Cognitive Engineering, AHFE Open Acces, 2022, doi: 10.54941/ahfe1001827.
D. G. Takale, “Revolutionizing Customer Interaction: NLP-Powered Virtual Shopping Assistants and Sentiment Analysis in E-commerce,” Journal of Firewall Software and Networking, vol. 2, no. 2, pp. 1–5, 2024, doi: 10.48001/jofsn.2024.221-5.
K. Machova, M. Mikula, X. Gao, and M. Mach, "Lexicon-based sentiment analysis using the Particle Swarm Optimization," Electronics, vol. 9, no. 8, Art. no. 8, Aug. 2020, doi: 10.3390/electronics9081317.
Y. Qi and Z. Shabrina, "Sentiment analysis using Twitter data: a comparative application of lexicon- and machine-learning-based approach," Social Network Analysis and Mining, vol. 13, no. 1, p. 31, Feb. 2023, doi: 10.1007/s13278-023-01030-x.
M. Yuzyk and P. Seidner, "E-Sports competitions," in Developments in Information & Knowledge Management for Business Applications: Volume 5, N. Kryvinska and M. Gregus, Eds., Cham: Springer International Publishing, 2022, pp. 671–716, doi: 10.1007/978-3-030-97008-6_30.
N. V. A. Cabiles, P. J. E. Samilo, Ma. S. Jane S. Magallanes, H. G. Adalia, and J. V. Chavez, "Qualitative exploration on the prevalence of swear words in MLBB among Filipino online players," Environmental and Social Psychology, vol. 10, no. 2, Feb. 2025, doi: 10.59429/esp.v10i2.3113.
W. Wang and J. and Fan, "Topic mining of real-time discussions: what catches the attention of live-streaming esports viewers?," European Sport Management Quarterly, vol. 24, no. 2, pp. 323–344, Mar. 2024, doi: 10.1080/16184742.2022.2117397.
S. R. Goniwada, "Sentiment analysis," in Introduction to Datafication: Implement Datafication Using AI and ML Algorithms, S. R. Goniwada, Ed., Berkeley, CA: Apress, 2023, pp. 165–184, doi: 10.1007/978-1-4842-9496-3_6.
P. Srinivas, K. Gayathri, K. Bhavitha, Jahnavi, and K. D. Sarath, "BLIP-NLP model for sentiment analysis," in 2023 2nd International Conference on Edge Computing and Applications (ICECAA), Jul. 2023, pp. 468–475, doi: 10.1109/ICECAA58104.2023.10212253.
L. Yuan, M. Ding, F. Meng, and Y. Tian, "Sentiment analysis and rating video game dimensions via NLP, " Applied and Computational Engineering, vol. 132, pp. 1–10, Jan. 2025, doi: 10.54254/2755-2721/2024.20540.
J. Watson et al., "Natural Language Processing to assess communication dynamics between cooperating dyads during video gameplay," in Neuroergonomics and Cognitive Engineering, AHFE Open Acces, 2022, doi: 10.54941/ahfe1001827.
D. G. Takale, “Revolutionizing Customer Interaction: NLP-Powered Virtual Shopping Assistants and Sentiment Analysis in E-commerce,” Journal of Firewall Software and Networking, vol. 2, no. 2, pp. 1–5, 2024, doi: 10.48001/jofsn.2024.221-5.
K. Machova, M. Mikula, X. Gao, and M. Mach, "Lexicon-based sentiment analysis using the Particle Swarm Optimization," Electronics, vol. 9, no. 8, Art. no. 8, Aug. 2020, doi: 10.3390/electronics9081317.
Y. Qi and Z. Shabrina, "Sentiment analysis using Twitter data: a comparative application of lexicon- and machine-learning-based approach," Social Network Analysis and Mining, vol. 13, no. 1, p. 31, Feb. 2023, doi: 10.1007/s13278-023-01030-x.
I. H. Hafidz, A. Sulistya, and B. R. Lidiawaty, "Sentiment analysis of public complaints: A machine learning comparison of SVM, Naive Bayes, Random Forest, and XGBoost," in 2025 International Conference on Advancement in Data Science, E-learning and Information System (ICADEIS), Feb. 2025, pp. 1–6, doi: 10.1109/ICADEIS65852.2025.10933382.
K. Suresh Kumar et al., "Sentiment analysis of short texts using SVMs and VSMs-Based multiclass semantic classification," Applied Artificial Intelligence, vol. 38, no. 1, p. 2321555, Dec. 2024, doi: 10.1080/08839514.2024.2321555.
Y. Yu, B.-H. Nguyen, F. Yu, and V.-N. Huynh, "Discovering topics of interest on steam community using an LDA approach," in Advances in the Human Side of Service Engineering, C. Leitner, W. Ganz, D. Satterfield, and C. Bassano, Eds., Cham: Springer International Publishing, 2021, pp. 510–517, doi: 10.1007/978-3-030-80840-2_59.
Y. Yu, D.-T. Dinh, B.-H. Nguyen, F. Yu, and V.-N. Huynh, "Mining insights from esports game reviews with an aspect-based sentiment analysis framework," IEEE Access, vol. 11, pp. 61161–61172, 2023, doi: 10.1109/ACCESS.2023.3285864.
I. K. S. Yogatama, A. P. Kharisma, and L. Fanani, "Analisis faktor-faktor yang memengaruhi minat pemain dalam permainan MOBA (Studi Kasus: Mobile Legends: Bang-Bang!)," Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, vol. 3, no. 3, pp. 2558–2566, Jan. 2019.
N. V. Babu and E. G. M. Kanaga, "Sentiment analysis in social media data for depression detection using artificial intelligence: A Review," SN Computer Science, vol. 3, no. 1, p. 74, Nov. 2021, doi: 10.1007/s42979-021-00958-1.
M. Smieja, J. Tabor, and P. Spurek, "SVM with a neutral class," Pattern Analysis and Applications, vol. 22, no. 2, pp. 573–582, May 2019, doi: 10.1007/s10044-017-0654-3.
V. K. Chauhan, K. Dahiya, and A. Sharma, "Problem formulations and solvers in linear SVM: A review," Artificial Intelligence Review, vol. 52, no. 2, pp. 803–855, Aug. 2019, doi: 10.1007/s10462-018-9614-6.
D. Rani, E. J. Hasibuan, and R. K. I. Barus, "Dampak game online mobile legends: Bang Bang terhadap mahasiswa," Jurnal Governansi Sosial Politikol, vol. 7, no. 1, pp. 6–12, 2018, doi: 10.31289/perspektif.v7i1.2520.
M. Khder, "Web scraping or web crawling: state of art, techniques, approaches and application", International Journal of Advances in Soft Computing and Its Applications, vol. 13, no. 3, p. 145-168, 2021, doi:10.15849/ijasca.211128.11
S. S. Sohail et al., "Crawling Twitter data through API: A technical/legal perspective," May 22, 2021, arXiv: arXiv:2105.10724, doi: 10.48550/arXiv.2105.10724.
S. Elbagir and J. Yang, "Sentiment analysis on Twitter with Python’s natural language toolkit and VADER sentiment analyzer," in IAENG Transactions on Engineering Sciences, WORLD SCIENTIFIC, 2019, pp. 63–80, doi: 10.1142/9789811215094_0005.
R. R. E. Agapito, K. B. Bayao, J. M. Casitas, and R. A. Sagum, "Tagnorm: Tagalog text normalization with slang word detection and classification of type of slang word using Support Vector Machine," in 2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), Nov. 2021, pp. 1–5, doi: 10.1109/HNICEM54116.2021.9731962.
J. H. Yu and D. Chauhan, "Trends in NLP for personalized learning: LDA and sentiment analysis insights," Education and Information Technologies, vol. 30, no. 4, pp. 4307–4348, Mar. 2025, doi: 10.1007/s10639-024-12988-2.
J. Jumadi, D. S. Maylawati, L. D. Pratiwi, and M. A. Ramdhani, "Comparison of Nazief-Adriani and Paice-Husk algorithm for Indonesian text stemming process," IOP Conference Series: Materials Science and Engineering, vol. 1098, no. 3, p. 032044, Mar. 2021, doi: 10.1088/1757-899X/1098/3/032044.
W. Ahmed, P. A. Bath, and G. Demartini, "Using Twitter as a data source: An overview of ethical, legal, and methodological challenges," in The Ethics of Online Research, vol. 2, Emerald Publishing Limited, 2017, pp. 79–107, doi: 10.1108/S2398-601820180000002004.