Cyberbullying Detection: A Logistic Regressio...

Cyberbullying Detection: A Logistic Regression Approach

Author Name : Sai Sudha Dorabala, Hardik Chauhan, Aditi Gatkul, Pallavi Gonjari

ABSTRACT This paper explores a novel system that employs Logistic Regression for the accurate detection of cyberbullying on social media. The study investigates results from various techniques for text classification, highlighting the integration of TF-IDF for efficient feature extraction and classification, making the system effective in addressing cyberbullying behaviors and promoting safer online communities. This study demonstrates how AI and machine learning technologies can enhance content moderation. Future advancements may include dataset augmentation, improved architectures, and the integration of advanced NLP techniques for enhanced real-time performance.