Toxinet: A Deep Learning Framework for Online Comment Toxicity Detection

The exponential growth of online interactions has brought unprecedented opportunities for global communication but has led to the proliferation of toxic content, including hate speech, harassment, and threats. This project leverages advancements in Deep Learning (DL) to develop a robust system for detecting and classifying toxic online comments. DL is used for nuanced text analysis, the system can identify toxicity level across multiple categories such as threats, insults, obscenity, and identify hate. The project utilizes Bidirectional LSTM-based multi-label classification model to process large-scale datasets like Kaggle's Wikipedia Talk Page edits. These models ensure accurate recognition of subtle linguistic patterns and contextual variations, enabling precise categorization of harmful content. The resulting framework aims to foster safer online environments by mitigating the impact of toxic communications, contributing to healthier digital interaction.