Social Unrest Prediction Through Sentiment Analysis on Twitter Using Support Vector Machine: Experimental Study on Nigeria’s #EndSARS

Abstract Social unrest is a powerful mode of expression and organized form behavior involving civil disorders acts mass disobedience, among other behaviors. Nowadays, signs most social start from the media websites, such as Twitter, Facebook, etc. In recent times, Nigeria has faced different forms unrest, including popular #EndSARS, which began on Twitter with demand that government disband Special Anti-Robbery Squad (SARS), unit under Nigerian Police Force for alleged brutality. Mining public opinions this can assist concerned organizations by serving an early warning system. work, we collected user tweets #EndSARS pre-processed annotated them into positive negative classes. A support vector classifier was then used classifying sentiment expressed in them. Experimental results show 90% accuracy, 94% precision, 85% recall, 89% F 1 score test set. The codes dataset are publicly available research use. https://github.com/Temidayomichael/Social-unrest-prediction .