DeepShield: A Deep Learning-Based Toxic Comment Detector

Abstract

In the contemporary digital landscape, the prevalence of toxic comments poses a significant challenge for online platforms, impacting user experiences and fostering hostile environments. To address the issue, we introduce "DeepShield," a state-of-the-art toxic comments detector built on deep learning techniques. DeepShield aims to automatically identify and flag toxic comments within online discussions, promoting healthier online communities. Leveraging a robust deep learning architecture with attention mechanisms, DeepShield captures context and nuances in language to effectively detect toxic behavior. The system is trained on a meticulously curated dataset, encompassing a wide range of toxic language to enhance model generalization. Beyond serving as a valuable tool for content moderation, DeepShield contributes to the discourse on fostering positive online interactions. By mitigating the impact of toxic comments, DeepShield strives to create safer and more inclusive online environments for all users.The objective of DeepShield is to automatically identify and flag toxic comments within online discussions, fostering healthier online communities. The system employs a robust deep learning architecture, and attention mechanisms to capture context and nuances in language. A meticulously curated dataset is used for training, incorporating a wide range of toxic language to enhance model generalization. This toxic comments detector not only serves as a valuable tool for content moderation but also contributes to the ongoing discourse on fostering positive online interaction