A Comparative Study and Analysis on Toxic Comment Classification

It is the task of identifying and categorizing comments that contain harmful or offensive content, such as hate speech, cyberbullying, or harassment. This task is crucial for maintaining a safe and respectful online community, but it poses several challenges due to the complexity and ambiguity of natural language, as well as the constantly evolving nature of toxic language. Recent techniques for toxic comment classification include deep learning models such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer-based models like BERT and GPT. These models have achieved impressive performance on benchmark datasets, but it still faces several challenges. One challenge is the lack of diversity in training data, which can lead to biased models that perform poorly on real-world data. Another challenge is the difficulty of detecting and classifying subtle forms of toxic language, such as sarcasm, irony, and euphemisms. The proposed objective of this study is to develop a more robust and accurate toxic comment classification system that addresses these challenges. Specifically, this research study aims to improve the model’s ability to detect and classify subtle forms of toxic language by incorporating additional contextual information and leveraging techniques such as adversarial training and data augmentation to increase the diversity of training data. This study also plans to evaluate the model’s performance on a range of real-world datasets to ensure its effectiveness in practical settings.