Hate Speech Detection in Tweets using Support Vector Machine

The proliferation of hate speech has grown to be a significant social worry because of social media’s explosive growth. This work aims to explain a new approach that blends Sentiment Analysis and Support Vector Machine (SVM) techniques. The objective is to build a robust and precise system that can identify hate speech instantly and promote a safer online community. Many trials are conducted in order to assess the system’s performance using metrics like accuracy, precision, recall, and F1-score. Modern hate speech identification technologies are used in comparison assessments to verify the superiority of the suggested method. It was found that using Sentiment Analysis and SVM together outperforms standard approaches, reaching comparatively large accuracy rates in detecting hate speech. In addition, this work investigates the ethical implications and future reach of algorithms that detect hate speech.