Characterization and mechanical properties of offensive language taxonomy and detection techniques

Abstract

Machine learning is the sub field of Artificial Intelligence which is responsible for developing a system that has the capability to learn and improve from past experience. There is an increasing demand for the offensive language detection because there is a large code-mixed text. Sometimes there is a possibility of code-mixed texts which are written using native scripts. If the system is getting trained on the data which is created using single language then it will leads to failure because of complexity. Using online communities and social media platforms has become increasing now. Recently many of the researchers have been investigating different ways of identifying the abusive content and developing systems to detect its different types like aggression identification, cyber bullying detection, hate speech identification, offensive language identification and toxic comments identification. Developing mechanisms to detect this is a challenging task. In the last few years, machine learning techniques and deep learning concept has been playing a significant role in the development of solutions for offensive language detection. So, considerable efforts have also been devoted to the development of machine learning techniques and deep learning models which is used to identify toxic content in recent years. First, this survey provides offensive language taxonomy and detection approaches. Then, the article focuses on the offensive language identification and toxic comments identification approaches. This article reviews various models used, type of learning model, languages for which the developed model is applied, other methods or approaches that were compared against this model, dataset used, offensive language detection type and performance metrics. The findings from the review are as well summarized and they urge that further effort is required to get improvement in the current state-of-the art approaches. This article also focuses on future work to improve the offensive language classification and toxic comments identification.