Developing an Efficient Toxic Comment Detector Using Machine Learning Techniques

Social media has changed the way people communicate, but it has also become a breeding ground for dangerous content. Natural Language Processing (NLP) is used in this study to classify unstructured data into dangerous and benign categories, providing insights about internet toxicity. The NLP approach used in the study gives light on the challenges and opportunities of toxicity identification. The researchers uncovered patterns and trends indicative of dangerous content by analysing massive amounts of text data, allowing them to construct powerful classification systems. The paper discusses the advantages and disadvantages of toxicity detection. Automated systems can swiftly scan enormous amounts of content, but they may misclassify some material, thereby leading to censorship or harassment. The online toxicity detection provide valuable guidance for stakeholders seeking to address this issue. By understanding the strengths and limitations of NLP-based approaches, informed decisions can be made about implementing effective toxicity detection strategies, ensuring a safer and more inclusive digital environment.