Toxicity Unveiled: Understanding Online Conversations

Internet forums enable people to express their opinions and participate in discussions on a range of subjects in the current digital era. But sometimes, this right to free speech can lead to the dissemination of harmful remarks like hate speech, harassment, and other types of damaging material. Ensuring a secure and courteous online space requires identifying and eliminating remarks that are harmful. This research article investigates many approaches to detecting poisonous comments, including classic machine learning techniques like BOW and TF-IDF, and deep learning methods like LSTM. In addition, we explore the performance of stochastic gradient descent (SGD) regressor and decision tree models in categorizing harmful remarks using these variables. Through rigorous research and review, we show that these approaches are effective at recognizing hazardous remarks. Our findings help to build strong models for automated toxic comment detection, promoting healthier online discourse.