Analysis and Detection of Hate Speech: A Comparative Study of NLP Transformer Models
Denis Cedeño-Moreno, Alan Delgado, Elia Esther Cano Acosta, Carlos A. Rovetto Rios, Miguel Vargas-Lombardo · 2024
Every minute millions of comments are posted about a certain context. People are becoming more and more involved with the wide spread of social media. It is so easy to post almost anything on social media, given the amount of unregulated content, which has given way to a proliferation of aggressive and harmful content commonly called hate speech on the Internet. One of the most important challenges in the area of Natural Language Processing (NLP) is the detection of semantic analysis in texts. We propose to design and develop a model for the automated detection of hate speech on social networks. A case study was carried out using a dataset extracted and labeled by us to evaluate the model, achieving excellent results.