Identification of Hate Speech using Natural Language Processing and Machine Learning

Over the last decade, social media has acquired a lot of traction, both positively and negatively. Way. With the fast growth of social networking, People can communicate with one other via platforms and websites. Directly with no cultural or economic barriers While There have been several advantages to using social media, yet there are none. Less negative societal effects One such issue that has arisen is Hate speech has been more prevalent in recent years. Hateful speeches essentially the use of rude and abusive words. Using social media It might relate to anybody or something specific. A collection of people who have common interests. In this study, we introduced our approach to dealing with hate speech and, to a considerable part, decreasing it. People express their hatred and rage on social media right instantly, which hurts the sentiments of others. It would have an extremely detrimental influence on their caste, creed, religion, and race. Some statements may not be intended to offend anyone, yet nonetheless are considered hate speech owing to the filthy language used. To eradicate hate speech, we dug deep into natural language processing and employed several machine learning models to choose which one to deploy based on accuracy.

Keywords: Natural Language Processing, Machine learning, Classification Technique and Logistic Regression