Public Sentiment Analysis with Opinion Mining on Rohingya Refugee Crisis in Bangladesh

Abstract

Twenty-first century, there has been moderate forced migration where it articulates human civilization. This forced migration is the identity of various territories and demography. According to this issue, Bangladesh is now the largest populated country for being stateless refugees in the world. The Rohingya community is hopeless about placing their lives, therefore, stateless refugees possessions in the southeastern border region of Bangladesh. Due to the part of Bangladesh addressing that stateless refugees and Bangladeshi peoples and other countries people have a tremendous debate on this issue. Tropically, according to these stateless refugees the victim of hate speech. This study aims to detect the daily report of negativity predicting actual analysis among machine learning approaches using hate speech features. Employed 5 classifiers like SVM, DT, KNN, RF, and NB. Necessary cleaning steps accommodate the model using preprocessing. NB shows the results outcome accuracy of 79% which is more than all other models. The approach of predicting hate speech on stateless refugees took 310 comments data which can rule as a global application..