Racism Detection by Analyzing Differential Opinions Through Sentiment Analysis of Tweets Using Stacked Ensemble GCR-NN Model

Abstract

With social media’s dominating role in the socio-political landscape, several existing and new forms of racism took place on social media. Racism has emerged on social media in different forms, both hidden and open, hidden with the use of memes and open as the racist remarks using fake identities to incite hatred, violence, and social instability. Although often associated with ethnicity, racism is now thriving based on color, origin, language, cultures, and most importantly religion. Social media opinions and remarks provocating racial differences have been regarded as a serious threat to social, political, and cultural stability and have threatened the peace of different countries. Consequently, social media being the leading source of racist opinions dissemination should be monitored and racism remarks should be detected and blocked timely. This study aims at detecting Tweets that contain racist text by performing the sentiment analysis of Tweets. Owing to the superior performance of deep learning, a stacked ensemble deep learning model is assembled by combining gated recurrent unit (GRU), convolutional neural networks (CNN), and recurrent neural networks RNN, called, Gated Convolutional Recurrent- Neural Networks (GCR-NN). GRU is on the top in the GCR-NN model to extract the suitable and prominent features from raw text, CNN extracts important features for RNN to make accurate predictions. Obviously, several experiments are conducted to investigate and analyze the performance of the proposed GCR-NN within the scope of machine learning and deep learning models indicating the superior performance of GCR-NN with increased 0.98 accuracy. The proposed GCR-NN model can detect 97% of the tweets that contain racist comments.