Cyberbullying, also known as cyber harassment, is a prevalent issue that significantly impacts the adolescent population in Malaysia. As a form of abusive online behavior, cyberbullying has expanded beyond the physical world into the virtual space, where monitoring activities and changes can be difficult. In 2020, Malaysia ranked second in Asia for cyberbullying among youths due to the widespread use of social media platforms, which have become an ideal breeding ground for negative behavior. This study aims to develop a web-based system that can analyze Malaysian citizens' emotional responses to cyberbullying using machine learning techniques. Additionally, emotional responses gained from Twitter will be characterized using emotional models. The machine learning life-cycle methodology will be implemented in this study. The findings revealed that anger emotion had the highest percentage compared to other emotions such as fear, happiness, love, sadness, and surprise. Expanding the scope of the study to include other social media platforms and demographic groups may provide a more comprehensive understanding of cyberbullying in Malaysia. These recommendations may improve the effectiveness of the web-based system and facilitate the development of more efficient strategies for preventing and mitigating the effects of cyberbullying in Malaysia.
Bernama. (2019). Specific law needed to stem cyberbullying. MalaysiaKini, 1. https://www.malaysiakini.com/news/501119
Aiken, M. (2016). The Cyber Effect: A Pioneering Cyber Psychologist Explains How Human Behavior Changes Online. Spiegel & Grau.
Bernama. (2022). Jangan mudah terjebak trend jelik. Harian Metro. https://www.hmetro.com.my/itmetro/2022/02/810464/jangan-mudah-terjebak-trend jelik
KABIR, A. I., KARIM, R., NEWAZ, S., & HOSSAIN, M. I. (2018). The Power of Social Media Analytics: Text Analytics Based on Sentiment Analysis and Word Clouds on R. Informatica Economica, 22(1/2018), 25–38. https://doi.org/10.12948/issn14531305/22.1.2018.03
Ozturk, K. Z., Cicek, E. Z. I., & Ergul, Z. (2017). Sentiment Analysis: An Application to Anadolu University. Acta Physica Polonica A, 132(3), 753– 755. https://doi.org/10.12693/APhysPolA.132.753
Martinus, D. (2021). Malaysia tightens security after #IsraelKoyak campaign goes viral. Mashable SEA. https://sea.mashable.com/culture/15691/malaysia-tightens-security-after- israelkoyak campaign-goes-viral
NST Online. (2022). Should we encourage cancel culture? NST Online. https://www.nst.com.my/opinion/letters/2022/03/782306/should- we-encourage cancel-culture
Spjuth, O., Frid, J., & Hellander, A. (2021). The machine learning life cycle and the cloud: Implications for drug discovery. Expert Opinion on Drug Discovery, 16, 1–9. https://doi.org/10.1080/17460441.2021.1932812
Talpur, B. A., & O’Sullivan, D. (2020). Cyberbullying severity detection: A machine learning approach. PLOS ONE, 15(10), e0240924.
https://doi.org/10.1371/journal.pone.0240924
Yunus, A. (2021). Malaysian solidarity for Palestinians changing international opinion on Israel. NST Online.
https://www.nst.com.my/news/nation/2021/05/691344/malaysian-
Ditchfield, J. L., Li, J., & Wang, Y. (2016). Cyberbullying in social media: A review of interventions. Journal of Social Media Studies, 1(1), 1-16.
Kabir, M. N. A., Arif, A. S. M., & Rahman, M. M. (2018). Sentiment analysis for Twitter data: A comprehensive review. Journal of Intelligent & Fuzzy Systems, 35(5), 5395-5414.
Liu, B. (2012). Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies, 5(1), 1-167.
Nazir, M. (2019). Death of Malaysian teenager renews calls for anti-cyberbullying law. Reuters. Retrieved from https://www.reuters.com/article/us-malaysia-cyberbullying-idUSKCN1S70RY
Davidov, D., Tsur, O., & Rappoport, A. (2010). Semi-supervised recognition of sarcastic sentences in Twitter and Amazon. In Proceedings of the Fourteenth Conference on Computational Natural Language Learning (pp. 107–116).
Jia, Y., & Liang, X. (2019). Ethics in machine learning. IEEE Intelligent Systems, 34(2), 54-59.
Kim, M., Park, H., & Lee, J. (2021). Cyberbullying detection on Korean social media using sentiment analysis and machine learning techniques. Journal of Information Processing Systems, 17(3), 747-757.
Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135.
Patchin, J. W., & Hinduja, S. (2015). Cyberbullying and self-esteem. Journal of School Health, 85(2), 79-88.
Rezvani, M., Hee Park, J., Pathak, N., Kobsa, A., & Terveen, L. (2017). Classification of cyberbullying roles using sentiment analysis. Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing, 1637-1650.
