Toxic Comment Classification

Toxic Comment Classification

1G Vikas, 2G Ravi Vardhan, 3G Somesh, 4Mr. Lalu Banothu

1,2,3 UG Scholars, 4 Associate Professor

1,2,3,4 Department of computer science Engineering,

1,2,3,4 Guru Nanak Institutions Technical Campus, Hyderabad, Telangana, India.

ABSTRACT:

Online abuse and harassment are escalating challenges in the cyber community, requiring robust and efficient content moderation strategies. This study investigates two approaches for identifying toxic comments: training individual classifiers for each facet of toxicity and treating the task as a multi-label classification problem. The facets of toxicity include behaviors like threats, insults, and hate speech, often exhibited simultaneously in a single comment. Using a Kaggle dataset and 10-fold cross-validation, machine learning models such as logistic regression, Naive Bayes, and decision trees are evaluated for performance. A novel preprocessing technique is proposed, transforming multi-label into multi-class classification, which significantly enhances model accuracy. Logistic regression emerges as the most effective model, outperforming others in both binary and multi-class classification tasks. These findings suggest the potential of this preprocessing strategy to boost the performance of more sophisticated neural network-based models, offering a scalable solution for real-world toxic content moderation systems.