Comparative Study of Machine Learning Methods for Sentiment Analysis of TikTok Comments  Related to Cyberbullying

Celestina Florecita Mariwy; Lorna Yertas Baisa; Andreas Leonardo Sumendap

doi:10.24014/ijaidm.v9i1.39183

Comparative Study of Machine Learning Methods for Sentiment Analysis of TikTok Comments Related to Cyberbullying

Celestina Florecita Mariwy, Lorna Yertas Baisa, Andreas Leonardo Sumendap

Abstract

The rapid growth of internet use in Indonesia has contributed to the rise of cyberbullying on TikTok, increasing the importance of automated sentiment analysis for digital safety. This study compares the performance of Support Vector Machine, K-Nearest Neighbors, and Naive Bayes in classifying sentiments in TikTok comments related to cyberbullying. The dataset was collected via web scraping and processed through several preprocessing stages, yielding 7,900 unique comments. Sentiment labeling used a lexicon-based approach, and the data were split into training and testing sets with an 80:20 ratio. Results show that 34.18% of comments were negative, indicating a notable level of harmful content. Among the three models, Support Vector Machine performed best with an accuracy of 91.5%, followed by Naive Bayes at 82.8% and K-Nearest Neighbors at 80.8%. These findings suggest Support Vector Machine is the most effective method for sentiment classification in this context and offer a useful reference for developing more accurate content moderation systems on social media.

Keywords

Cyber Bullying; K-Nearest Neighbors; Naive Bayes; Sentiment Analysis; Support Vector Machine

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References

A. Alamsyah and Y. Sagama, “Empowering Indonesian internet users: An approach to counter online toxicity and enhance digital well-being,” Intelligent Systems with Applications, vol. 22, p. 200394, Jun. 2024, doi: 10.1016/j.iswa.2024.200394.

A. Mishra, S. Sinha, and C. P. George, “Shielding against online harm: A survey on text analysis to prevent cyberbullying,” Eng. Appl. Artif. Intell., vol. 133, p. 108241, Jul. 2024, doi: 10.1016/j.engappai.2024.108241.

H. Yunhao, E. Sophie, C. Elizabeth M., and K. Bianca, “Player versus Player: A systematic review of cyberbullying in multiplayer online games,” Computers in Human Behavior Reports, vol. 18, p. 100675, May 2025, doi: 10.1016/j.chbr.2025.100675.

Z. Dong, Z. Wu, and X. Sun, “Follow the herd or your heart? The role of trait mindfulness in adolescents’ responses to observed cyberbullying,” Pers. Individ. Dif., vol. 243, p. 113228, Sep. 2025, doi: 10.1016/j.paid.2025.113228.

T. Mahmud, M. Ptaszynski, J. Eronen, and F. Masui, “Cyberbullying detection for low-resource languages and dialects: Review of the state of the art,” Inf. Process. Manag., vol. 60, no. 5, p. 103454, Sep. 2023, doi: 10.1016/j.ipm.2023.103454.

T. H. Teng, K. D. Varathan, and F. Crestani, “A comprehensive review of cyberbullying-related content classification in online social media,” Expert Syst. Appl., vol. 244, p. 122644, Jun. 2024, doi: 10.1016/j.eswa.2023.122644.

Y. Y. Zandroto, A. V. Vitianingsih, A. L. Maukar, N. K. Hikmawati, and R. Hamidan, “Sentiment Analysis of BCA Mobile App Reviews Using K-Nearest Neighbor and Support Vector Machine Algorithm,” Indonesian Journal of Artificial Intelligence and Data Mining, vol. 8, no. 2, p. 448, Aug. 2025, doi: 10.24014/ijaidm.v8i2.37773.

O. S. Jelni, M. L. Radhitya, G. W. Wardhana, Ni Wayan Jeri Kusuma, and N. M. M. R. Desmayani, “Sentiment Analysis of BRImo Reviews on Google Play Store Using SVM and KNN,” Indonesian Journal of Data and Science, vol. 6, no. 3, pp. 548–562, Dec. 2025, doi: 10.56705/ijodas.v6i3.365.

Ni Wayan Indah Juliandewi, A. S. Kusuma, K. M. D. Putri, I. G. A. Indrawan, and I. G. A. A. M. Aristamy, “Comparison of Naïve Bayes and Random Forest in Sentiment Analysis of State-Owned Banks Management by Danantara on X and YouTubeComparison of Naïve Bayes and Random Forest in Sentiment Analysis of State-Owned Banks Management by Danantara on X and YouTube,” Indonesian Journal of Data and Science, vol. 6, no. 3, pp. 527–537, Dec. 2025, doi: 10.56705/ijodas.v6i3.366.

J. Fillies et al., “A novel German TikTok hate speech dataset: far-right comments against politicians, women, and others,” Discover Data, vol. 3, no. 1, p. 4, Mar. 2025, doi: 10.1007/s44248-025-00020-y.

M. Alzaqebah et al., “Cyberbullying detection framework for short and imbalanced Arabic datasets,” Journal of King Saud University - Computer and Information Sciences, vol. 35, no. 8, p. 101652, Sep. 2023, doi: 10.1016/j.jksuci.2023.101652.

A. Akhter, U. K. Acharjee, Md. A. Talukder, Md. M. Islam, and M. A. Uddin, “A robust hybrid machine learning model for Bengali cyber bullying detection in social media,” Natural Language Processing Journal, vol. 4, p. 100027, Sep. 2023, doi: 10.1016/j.nlp.2023.100027.

A. M. Alduailaj and A. Belghith, “Detecting Arabic Cyberbullying Tweets Using Machine Learning,” Mach. Learn. Knowl. Extr., vol. 5, no. 1, pp. 29–42, Jan. 2023, doi: 10.3390/make5010003.

N. I. Boyko and V. Yu. Mykhailyshyn, “K-Nn’s Nearest Neighbors Method For Classifying Text Documents By Their Topics,” Radio Electronics, Computer Science, no. 3, p. 83, Oct. 2023, doi: 10.15588/1607-3274-2023-3-9.

B. Satya, M. H. S J, M. Rahardi, and F. F. Abdulloh, “Sentiment Analysis of Review Sestyc Using Support Vector Machine, Naive Bayes, and Logistic Regression Algorithm,” in 2022 5th International Conference on Information and Communications Technology (ICOIACT), IEEE, Aug. 2022, pp. 188–193. doi: 10.1109/ICOIACT55506.2022.9972046.

S. Tuarob, M. Satravisut, P. Sangtunchai, S. Nunthavanich, and T. Noraset, “FALCoN: Detecting and classifying abusive language in social networks using context features and unlabeled data,” Inf. Process. Manag., vol. 60, no. 4, p. 103381, Jul. 2023, doi: 10.1016/j.ipm.2023.103381.

R. Rahmaddeni and F. Akbar, “Comparison of Naïve Bayes Algorithm, Support Vector Machine and Decision Tree in Analyzing Public Opinion on COVID-19 Vaccination in Indonesia,” Indonesian Journal of Artificial Intelligence and Data Mining, vol. 6, no. 1, p. 8, Apr. 2023, doi: 10.24014/ijaidm.v6i1.19966.

M. Al-Hashedi, L.-K. Soon, H.-N. Goh, A. H. L. Lim, and E.-G. Siew, “Cyberbullying Detection Based on Emotion,” IEEE Access, vol. 11, pp. 53907–53918, 2023, doi: 10.1109/ACCESS.2023.3280556.

A. Almomani, K. Nahar, M. Alauthman, M. A. Al-Betar, Q. Yaseen, and B. B. Gupta, “Image cyberbullying detection and recognition using transfer deep machine learning,” International Journal of Cognitive Computing in Engineering, vol. 5, pp. 14–26, 2024, doi: 10.1016/j.ijcce.2023.11.002.

S. Hu, W. Lei, H. Zhu, and C. Hsu, “Cyberbullying perpetration on social media: A situational action perspective,” Information & Management, vol. 61, no. 6, p. 104013, Sep. 2024, doi: 10.1016/j.im.2024.104013.

R. Alsheikh, E. Fadel, and N. Akkari, “An Adaptive State Consistency Architecture for Distributed Software-Defined Network Controllers: An Evaluation and Design Consideration,” Applied Sciences, vol. 14, no. 6, p. 2627, Mar. 2024, doi: 10.3390/app14062627.

A. C. Roy, T. Mahmud, and T. Abrar, “A multi-class cyberbullying classification on image and text in code-mixed Bangla-English social media content,” Natural Language Processing Journal, vol. 13, p. 100191, Dec. 2025, doi: 10.1016/j.nlp.2025.100191.

S. Cirillo, D. Desiato, G. Polese, G. Solimando, V. Sugumaran, and S. Sundaramurthy, “Exploring the ability of emerging large language models to detect cyberbullying in social posts through new prompt-based classification approaches,” Inf. Process. Manag., vol. 62, no. 3, p. 104043, May 2025, doi: 10.1016/j.ipm.2024.104043.

A. A. Jamjoom, H. Karamti, M. Umer, S. Alsubai, T.-H. Kim, and I. Ashraf, “RoBERTaNET: Enhanced RoBERTa Transformer Based Model for Cyberbullying Detection With GloVe Features,” IEEE Access, vol. 12, pp. 58950–58959, 2024, doi: 10.1109/ACCESS.2024.3386637.

M. K. Mali et al., “Automatic detection of cyberbullying behaviour on social media using Stacked Bi-Gru attention with BERT model,” Expert Syst. Appl., vol. 262, p. 125641, Mar. 2025, doi: 10.1016/j.eswa.2024.125641.

S. Ullah, M. Kukreti, A. Sami, M. R. Shaukat, and A. Dangwal, “The role of bystander behavior and employee resilience in mitigating workplace cyberbullying impacts on employee innovative performance,” Human Systems Management, vol. 44, no. 4, pp. 629–640, Jul. 2025, doi: 10.1177/01672533251317066.

K. Subhashree and S. M. Kumar, “Enhanced quantum long short-term memory neural network based multi-task learning for sentimental analysis and cyberbullying detection,” Expert Syst. Appl., vol. 282, p. 127555, Jul. 2025, doi: 10.1016/j.eswa.2025.127555.

M. Karpagam et al., “An effective cyberbullying-flashing identification on whatsapp using PTS-GReLU-GRU with harmful level prediction,” Sci. Rep., vol. 16, no. 1, p. 80, Dec. 2025, doi: 10.1038/s41598-025-28765-1.

DOI: http://dx.doi.org/10.24014/ijaidm.v9i1.39183

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