Automated offensive language classification through “emotional” comments from network users

Abstract

In recent years, online hate speech has become widespread as a result of the ease with which any online opinion can be presented on social media. This trend is mainly due to the rapid growth of mobile devices, computers and the Internet. There is a lot of interest in research on automatic hate speech detection. However, there is not a lot research on whether social networking sites can help identify hate crimes and related communities. Hate speech has a negative impact on the mental level of any person. However, some people are more prone to emotional instability than others. It is difficult for racial or ethnic groups whose language is not popular automatically collecting and evaluate texts created on the Internet. In this article, using artificial intelligence techniques in text processing and natural language processing, for the first time, a database was collected using various keywords to identify offensive words in social networks.