Racial Slur detection using Natural Language Processes

Any statement or phrase that disparages a group of people because of their gender identity, religion, ethnicity, national origin, race, sexual orientation, or is referred to as a racial slur. Hate, mistrust, and intolerance are often propagated via hate speech. It may also be used to harm or frighten others. People may feel alienated, anxious, and exposed as a result. Hate crimes may also result from it. Furthermore, hate speech may undermine intergroup trust. It is crucial to identify hate speech since doing so may assist to stop these negative impacts. Social media has recently developed into a haven for hate speech. Social media hate speech may be detrimental to society. Free speech, racial slur, and laws not for it have all been striving issues. Any speech, behaviour, writing, or display that encourages violence or other unpleasant deeds against a cohort of people or an organisation based on that person's subscription in the group, or that defames or affects that person or personal based on that membership, is considered racial slur under the laws of most countries. The laws may designate a category based on particular identities. Many countries do not consider racial slur to be a legal word. Additionally, the constitution permits the majority of what is seen as “hate speech” in many countries, including the US. In different countries, a sufferer of racial slurs may seek justice through civil lawsuit, convict lawsuit, or both. As communications are delivered and received virtually instantly, social media networks (SMNs) are the quickest form of communication. The emergence of social media platforms has irreparably altered how people interact in our society, and one result of these changes is an increase in unacceptable behaviours like the use of nasty and angry language online. It might be challenging to express harsh or disrespectful ideas to someone right in the face. People believe it is safe to abuse people or publish harmful content online. Hate speech may be harmful to an individual or a community. For improper material on the Web to be changed or blocked, it must be detected. Massive amounts of text data need human processing and classification, which takes time and effort. But since there is so much data released each day, automated approaches are crucial for recognising this kind of material.