• Vishwa K. Patel
• Gaurav D. Tivari
• Paras Narkhede
• Dr. Satvik Khara

Social media has become a dominant platform in modern culture but has also seen a rise in issues like cyberbullying and online harassment, impacting people's mental health. Researchers are increasingly focusing on identifying indicators of online abuse, aiming to develop a system that can detect such instances using Natural Language Processing and Random Forest regression. The COVID-19 pandemic has exacerbated cyberbullying, especially among young people, due to changes in social interactions online. Proposed solutions include data cleansing, text mining with lemmatization, feature extraction with VADER emotion analysis, and data classification using Naive Bayes and VADER sentiment for word vector generation.

Cyberbullying Detection, Machine learning algorithms, social media, Twitter, Feature engineering, Rumour detection, natural language processing

"Automated Cyberbullying Detection Leveraging Data Science and Machine Learning Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 7, page no.f123-f128, July-2024, Available :http://www.jetir.org/papers/JETIR2407520.pdf

"Automated Cyberbullying Detection Leveraging Data Science and Machine Learning Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 7, page no. ppf123-f128, July-2024, Available at : http://www.jetir.org/papers/JETIR2407520.pdf