A Comparative Analysis of hate speech detection models based on supervised machine learning

With the easy accessibility of internet in the last decade, the number of internet users has also risen drastically.The number of people consuming digital content in the recent years has grown in such a way that monitoring the behavior of these digital persons on the digital platforms like social media have become next to impossible without the assistance of the computational resources.The computational process and artificial intelligence in particular have become front runners in full-filling the demand of analysis of people behavior and hate speech by them in digital environment.The machine learning has provided a number of different models and techniques to gauge the hate speech using textual data on different cases and requirements.Through this paper we are trying to analyze the different machine learning models based on common feature engineering and supervised learning approach and try to get insight of different models and their efficacies.The machine learning models are being trained on the common dataset of text of English language and in Roman script.