Abusive language detection from social media comments using conventional machine learning and deep learning approaches

With the increase in the culture of social media and netizen, every day, millions of comments are posted on the uploaded posts. The use of abusive language in user comments has been increased rapidly. Abusive language in online comments initiates cyber-bullying that targets individuals (celebrity, politician, and product) and a group of people (specific country, age, and religion). It is important to detect and analyze abusive language from online comments automatically. There have been several attempts in the literature to detect abusive language for English. In this study, we perform abusive language detection from Urdu and Roman Urdu comments using five diverse ML models (NB, SVM, IBK, Logistic, and JRip) and four DL models (CNN, LSTM, BLSTM, and CLSTM). We apply these models on a large dataset with ten thousands of Roman Urdu comments and a small dataset with more than two thousand comments of Urdu. Natural language constructs, English-like nature of Roman Urdu script, and Nastaleeq style of Urdu make it more challenging to process and classify the comments of both scripts using deep learning and machine learning approaches. From experiments, we find that the convolutional neural network outperforms the other models and achieves 96.2% and 91.4% accuracy on Urdu and Roman Urdu. Our results also reveal that the one-layer architectures of deep learning models give better results than two-layer architectures. Further, we compare the performance of deep learning models with five conventional machine learning models and conclude that deep learning models perform significantly better than machine learning models.